Intelligent transportation systems including digital twin interfaces for passenger cars

Abstract

A transportation system can present a set of vehicle operating states to a user of the vehicle and generally includes a system for presenting a set of vehicle operating states to a user of the vehicle, the system including a portion of a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin system to present the vehicle operating states to the user of the vehicle.

Description

Technical Field

[0001] (Cross - reference to related applications) This application claims priority to U.S. Provisional Application No. 62 / 984,225, filed on March 2, 2020, entitled "Intelligent Transportation Systems including Digital Twin Interface for a Passenger Vehicle", and also claims priority to U.S. Provisional Application No. 63 / 069,537, filed on August 24, 2020, entitled "Information Technology Systems and Methods for Transportation Artificial Intelligence Leveraging Digital Twins", which are hereby incorporated by reference in their entirety as if fully set forth herein.

[0002] This disclosure relates to digital twins for passenger vehicles, and more particularly to a configurable digital twin interface for presenting a series of vehicle states to a vehicle user.

Background Art

[0003] A digital twin is a digital representation of information about a machine, physical device, system, process, person, etc. Once created, a digital twin can be used to represent a machine as a digital representation of a real - world system. A digital twin is created to have the same shape and behavior as the corresponding machine. Further, a digital twin can reflect the state of a machine within a larger system. For example, sensors can be installed on a machine to obtain real - time (or near - real - time) data from a physical object and relay it to a remote digital twin.

[0004] Generally, digital twins are used to simulate or mimic the operation of machines or physical devices in a virtual world. In this context, digital twins can display the structural components of a machine, illustrate its lifecycle or design steps, and be viewable through a user interface.

[0005] Digital twins are utilized in a variety of applications in the automotive industry. Such twins are used by designers and product developers when developing new vehicles. Digital twins allow engineers to model and test new safety features in vehicles, and enable manufacturers to improve various components and subsystems of vehicles. However, the application of digital twins to manage and improve the customer experience for users, including vehicle owners and drivers, has been limited. Furthermore, digital twin interfaces are quite rudimentary and typically do not offer options for configuring or customizing the interface based on user profiles. The applicant recognized the need and opportunity for a digital twin system for passenger cars. [Overview of the project]

[0006] In particular, provided herein are methods, systems, components, processes, modules, blocks, circuits, subsystems, articles, and other elements (collectively referred to as "platforms" or "systems" in some cases, and these terms should be understood to encompass either of the above unless otherwise specified in the context).

[0007] In one embodiment, a system for showing a set of vehicle operating states to a vehicle user includes a part of the vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface of the digital twin system for presenting the vehicle operating states to the vehicle user.

[0008] In an embodiment, the vehicle operating state is the vehicle maintenance state. In an embodiment, the vehicle operating state is the vehicle energy utilization state. In an embodiment, the vehicle operating state is the vehicle navigation state. In an embodiment, the vehicle operating state is the vehicle component state. In an embodiment, the vehicle operating state is the vehicle driver state. In an embodiment, the input to the digital twin system includes at least one of an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or an external system.

[0009] In an embodiment, the system includes an identity management system for managing a set of vehicle user identities and roles. In an embodiment, the identity management system includes the ability to view, modify, and configure a digital twin system based on identities from a set of vehicle user identities. In an embodiment, the digital twin system is populated via API from a vehicle edge intelligence system that provides 5G connectivity to systems outside the vehicle. In an embodiment, the digital twin system is populated via API from a vehicle edge intelligence system that provides internal 5G connectivity to a set of vehicle sensors and data sources. In an embodiment, the digital twin system is populated via API from a vehicle edge intelligence system that provides 5G connectivity to an in-vehicle artificial intelligence system.

[0010] In one embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a set of digital twin users. In another embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a driver user. In yet another embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a rider user.

[0011] In one embodiment, the system includes a first neural network that detects the detected satisfaction state of a rider user riding in a vehicle through analysis of data collected from sensors deployed in the vehicle to collect the rider user's physiological state, and a second neural network that optimizes the vehicle's operating parameters according to the detected satisfaction state of the rider user in order to achieve a preferred satisfaction state for the rider user.

[0012] In an embodiment, the detected satisfaction state of the rider user is the detected emotional state of the rider user. In an embodiment, the preferred satisfaction state of the rider user is the preferred emotional state of the rider user. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the second neural network optimizes operating parameters based on the correlation between the vehicle's operating state and the rider user's rider satisfaction state. In an embodiment, the second neural network optimizes operating parameters in real time in response to the detection of the rider user's detected satisfaction state by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to objects along the path, and its approach to other vehicles along the path.

[0013] In one embodiment, a method for presenting a set of vehicle states to a vehicle user includes obtaining parameter data for one or more components of the vehicle from one or more inputs, updating a digital twin of the vehicle with the parameter data to generate one or more operating states of the vehicle, and providing an interface for presenting one or more operating states of the vehicle to a vehicle user.

[0014] In an embodiment, one or more vehicle operating states include one or more of the following: vehicle maintenance state, vehicle energy utilization state, vehicle navigation state, vehicle component state, or vehicle driver state. In an embodiment, the input for the digital twin system includes one or more of the following: an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or an external system.

[0015] In an embodiment, the method includes managing a set of vehicle user identities and roles, and configuring a digital twin system based on identities from the set of vehicle user identities.

[0016] In an embodiment, the method includes inputting data to a digital twin system via an API from a vehicle edge intelligence system configured to have one or more of the following: 5G connectivity to external systems of the vehicle, internal 5G connectivity to a set of sensors and data sources of the vehicle, or 5G connectivity to an in-vehicle artificial intelligence system.

[0017] In one embodiment, the method includes automatically configuring a digital twin system using an artificial intelligence system based on a training set of usage activities by a set of digital twin users.

[0018] In an embodiment, the method includes detecting the detected satisfaction state of a rider user riding in a vehicle by analyzing data collected from sensors deployed in the vehicle to collect the rider user's physiological state using a first neural network, and optimizing the vehicle's operating parameters using a second neural network in accordance with the detected satisfaction state of the rider user in order to achieve a preferred satisfaction state for the rider user.

[0019] In the embodiment, the detected satisfaction state of the rider user is the detected emotional state of the rider user. In the embodiment, the rider user's preferred satisfaction state is the rider user's preferred emotional state.

[0020] In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the optimization of operating parameters by the second neural network is performed based on the correlation between the vehicle operating state and the rider user's rider user satisfaction state. In an embodiment, the optimization of operating parameters by the second neural network is performed in response to the detection of the rider user's detected satisfaction state by the first neural network.

[0021] In embodiments, the method includes using a first neural network to form one or more connected nodes that form a directed cycle. In embodiments, the first neural network further facilitates the bidirectional flow of data between the connected nodes. In embodiments, the operating parameters to be optimized affect at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, the approach to objects along the path, and the approach to other vehicles along the path.

[0022] In an embodiment, a computer-implemented method for generating a digital twin of a vehicle includes: receiving a request from a vehicle user via an interface to display vehicle status information; generating a digital twin representation of the vehicle based on one or more user inputs based on the vehicle status information using one or more processors; and displaying the vehicle status information using the digital twin representation of the vehicle via the interface. In an embodiment, the vehicle status information includes one or more of the following: vehicle maintenance status, vehicle energy utilization status, vehicle navigation status, vehicle component status, or vehicle driver status. In an embodiment, the user input for the digital twin representation includes one or more of the following: an in-vehicle diagnostic system, a telemetry system, a vehicle-installed sensor, or an external system.

[0023] In embodiments, the method includes managing a set of vehicle user identities and roles, and configuring a digital twin representation based on identities from the set of vehicle user identities. In embodiments, the method includes inputting the digital twin representation via an API from a vehicle edge intelligence system configured with one or more of the following: 5G connectivity to external systems, internal 5G connectivity to a set of vehicle sensors and data sources, or 5G connectivity to an in-vehicle artificial intelligence system. In embodiments, the method includes automatically configuring the digital twin representation in an artificial intelligence system based on a training set of usage activities by the set of digital twin users.

[0024] In one embodiment, a system for presenting a set of vehicle operating states to a vehicle driver includes a part of a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin system for presenting the vehicle operating states to the vehicle driver.

[0025] In embodiments, the interface for the digital twin system provides the driver with a 3D view showing a three-dimensional rendering of the vehicle model. In embodiments, the interface for the digital twin system provides the driver with a navigation view to improve situational awareness through real-time information exchange with nearby vehicles, pedestrians, and infrastructure. In embodiments, the interface for the digital twin system provides the driver with an energy view to determine the battery / fuel status in the vehicle, including utilization and battery health. In embodiments, the interface for the digital twin system provides the driver with a value view to display the vehicle status and blue book value based on the vehicle status. In embodiments, the interface for the digital twin system provides the driver with a service and maintenance view to display wear and failure of vehicle components and predict the need for service, repair, or replacement based on the vehicle status conditions. In embodiments, the interface for the digital twin system provides the driver with a world view to show the vehicle immersed in a virtual reality (VR) environment. In embodiments, the vehicle operating state is the vehicle maintenance state. In embodiments, the vehicle operating state is the vehicle energy utilization state. In embodiments, the vehicle operating state is the vehicle navigation state. In embodiments, the vehicle operating state is the vehicle component state. In the embodiment, the vehicle operating state is the vehicle driver state. In the embodiment, the input for the digital twin system includes at least one of the following: an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or an external system.

[0026] In an embodiment, the system includes an identity management system for managing a set of user identities and roles of a vehicle. In an embodiment, the identity management system includes functions for browsing, modifying, and configuring a digital twin system, and is based on an identity from a set of user identities of the vehicle. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides a 5G connection to a system external to the vehicle. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides an internal 5G connection to a set of vehicle sensors and data sources. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides a 5G connection to an in-vehicle artificial intelligence system.

[0027] In an embodiment, the digital twin system is automatically set by an artificial intelligence system based on a training set of usage activities by a set of digital twin users. In an embodiment, the digital twin system is automatically set by an artificial intelligence system based on a training set of usage activities by a driver. In an embodiment, the digital twin system is automatically set by an artificial intelligence system based on a training set of usage activities by a driver.

[0028] In an embodiment, the system includes a first neural network that detects a detected satisfaction state of a driver boarding the vehicle through analysis of data collected from sensors deployed in the vehicle to collect the driver's physiological state, and a second neural network that optimizes the operating parameters of the vehicle according to the detected satisfaction state of the driver in order to achieve the driver's preferred satisfaction state.

[0029] In an embodiment, the detected satisfaction state of the driver is the detected emotional state of the driver. In an embodiment, the preferred satisfaction state of the driver is the preferred emotional state of the driver. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network including a convolutional neural network. In an embodiment, the second neural network optimizes an operation parameter based on a correlation between a vehicle operation state and a driver satisfaction state of the driver. In an embodiment, the second neural network optimizes the operation parameter in real time in response to detection of the detected satisfaction state of the driver by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes forming a directed cycle, and the first neural network further facilitates a bidirectional flow of data between the connected nodes. In an embodiment, the operation parameter to be optimized affects at least one of a vehicle route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, approach to an object along the route, and approach to another vehicle along the route.

[0030] In an embodiment, a system for presenting a set of vehicle operation states to a vehicle manufacturer includes a vehicle having a vehicle operation state, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operation state, and an interface for the digital twin system for presenting the vehicle operation state to the vehicle manufacturer.

[0031] In embodiments, the interface for the digital twin system provides the manufacturer with a design view to inform the manufacturer when determining parts of the optimal system architecture of a new vehicle model through generative design techniques. In embodiments, the interface for the digital twin system provides the manufacturer with parts of an assembly view to enable the manufacturer to run a prescriptive model to optimize the performance of the vehicle and its components and subsystems. In embodiments, the interface provides the manufacturer with parts of a quality view to enable the manufacturer to run simulations to quality test the vehicle and its components in real-world conditions. In embodiments, the interface provides the manufacturer with a real-time analysis view to enable the manufacturer to perform data analysis to calculate a wide range of metrics, build charts, graphs, and models, and visualize the impact of changes in vehicle and component parameters on vehicle performance. In embodiments, the interface for the digital twin system provides the manufacturer with a 3D view showing a three-dimensional rendering of the vehicle model. In embodiments, the interface for the digital twin system provides the manufacturer with an energy view to determine the battery / fuel status in the vehicle, including utilization and battery health. In an embodiment, the interface for the digital twin system provides the manufacturer with a value view to display the vehicle status and bluebook value based on the vehicle's condition. In an embodiment, the interface for the digital twin system provides the manufacturer with a service and maintenance view to display wear and failure of vehicle components and to predict the need for service, repair, or replacement based on the vehicle condition. In an embodiment, the vehicle operating state is the vehicle maintenance state. In an embodiment, the vehicle operating state is the vehicle energy utilization state. In an embodiment, the vehicle operating state is the vehicle navigation state. In an embodiment, the vehicle operating state is the vehicle component state.In an embodiment, the vehicle operating state is the vehicle driver state. In an embodiment, one or more inputs for the digital twin system include at least one of an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or an external system.

[0032] In an embodiment, the system includes an identity management system that manages a set of vehicle user identities and roles, for which consent for communication has been granted to the manufacturer. In an embodiment, the identity management system includes the ability to view, modify, and configure a digital twin system, which is based on identities from a set of vehicle user identities. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides 5G connectivity to systems outside the vehicle. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides internal 5G connectivity to a set of vehicle sensors and data sources. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides 5G connectivity to an in-vehicle artificial intelligence system. In an embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities by a set of digital twin users. In an embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities by either the driver or the manufacturer.

[0033] In one embodiment, the system includes a first neural network that detects the detected satisfaction state of a user riding in a vehicle through analysis of data collected from sensors deployed in the vehicle to collect the user's physiological state, and a second neural network that optimizes the vehicle's operating parameters according to the detected satisfaction state of the user in order to achieve the user's preferred satisfaction state.

[0034] In an embodiment, the user's detected satisfaction state is the user's detected emotional state. In an embodiment, the user's preferred satisfaction state is the user's preferred emotional state. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the second neural network optimizes operating parameters based on the correlation between the vehicle operating state and the user's satisfaction state. In an embodiment, the second neural network optimizes operating parameters in real time in response to the detection of the user's detected satisfaction state by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to objects along the path, and its approach to other vehicles along the path.

[0035] In an embodiment, a system for presenting a set of vehicle operating states to a vehicle dealer includes a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating state, and an interface for the digital twin system for presenting the vehicle operating states to the vehicle dealer. In an embodiment, the interface provides the dealer with a configurator view for configuring the vehicle based on potential customer preferences by selecting compatible component and subsystem combinations from a set of available components. In an embodiment, the interface provides the dealer with a performance tuning view for modifying or adjusting the characteristics of one or more components to personalize the vehicle's performance based on driver or rider preferences.

[0036] In an embodiment, one of the components is the vehicle's engine. In an embodiment, one or more of the components are the vehicle's suspension. In an embodiment, one of the components is the vehicle's body.

[0037] In one embodiment, the interface provides a dealer with a test drive view, enabling potential customers of the vehicle to virtually test drive the vehicle. In one embodiment, the vehicle is a used vehicle.

[0038] In one embodiment, the interface provides the dealer with a certification view to enable the dealer to provide a certificate regarding the condition of the vehicle to a potential customer. In one embodiment, the vehicle is a used vehicle.

[0039] In an embodiment, the interface provides the dealer with a portion of the quality view to enable the dealer to run simulations to quality test the vehicle and its components in real-world conditions. In an embodiment, the interface for the digital twin system provides the dealer with a 3D view showing a three-dimensional rendering of the vehicle model. In an embodiment, the interface for the digital twin system provides the dealer with a value view to display the vehicle's condition and bluebook value based on the vehicle's state. In an embodiment, the interface for the digital twin system provides the dealer with a service and maintenance view to display wear and failure of the vehicle's components and to predict the need for service, repair, or replacement based on the vehicle's condition.

[0040] In an embodiment, the vehicle operating state is the vehicle maintenance state. In an embodiment, the vehicle operating state is the vehicle energy utilization state. In an embodiment, the vehicle operating state is the vehicle navigation state. In an embodiment, the vehicle operating state is the vehicle component state. In an embodiment, the vehicle operating state is the vehicle driver state. In an embodiment, one or more inputs for the digital twin system include at least one of an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or a system outside the vehicle.

[0041] In an embodiment, the system includes an identity management system for managing a set of vehicle user identities and roles for which consent for communication has been granted to the dealer. In an embodiment, the identity management system includes the ability to view, modify, and configure a digital twin system which is based on identities from a set of vehicle user identities.

[0042] In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides 5G connectivity to systems outside the vehicle. In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides internal 5G connectivity to a set of sensors and data sources within the vehicle. In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides 5G connectivity to an in-vehicle artificial intelligence system.

[0043] In one embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities by a set of digital twin users. In another embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities by either a driver or a dealer.

[0044] In one embodiment, the system includes a first neural network that detects the detected satisfaction state of a user riding in a vehicle through analysis of data collected from sensors deployed in the vehicle to collect the user's physiological state, and a second neural network that optimizes the vehicle's operating parameters according to the detected satisfaction state of the user in order to achieve the user's preferred satisfaction state.

[0045] In an embodiment, the user's detected satisfaction state is the user's detected emotional state. In an embodiment, the user's preferred satisfaction state is the user's preferred emotional state. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the second neural network optimizes operating parameters based on the correlation between the vehicle operating state and the user's satisfaction state. In an embodiment, the second neural network optimizes operating parameters in real time in response to the detection of the user's detected satisfaction state by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to objects along the path, and its approach to other vehicles along the path.

[0046] In one embodiment, a system for presenting a set of vehicle operating states to a vehicle owner includes a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin system for presenting the vehicle operating states to the vehicle owner.

[0047] In embodiments, the interface for the digital twin system provides the owner with a fleet monitoring view to track and monitor the movement / route / status of one or more vehicles. In embodiments, the interface for the digital twin system provides the owner with a driver behavior monitoring view to enable the owner to monitor instances of unsafe or dangerous driving by drivers. In embodiments, the interface for the digital twin system provides the owner with an insurance view to assist the owner in determining an insurance contract estimate for the vehicle based on the vehicle's status. In embodiments, the interface for the digital twin system provides the owner with a compliance view to show compliance status regarding exhaust emissions / pollutants and other regulatory standards based on the vehicle's status. In embodiments, the interface provides the owner with a performance tuning view to modify or adjust the characteristics of one or more components to personalize the vehicle's performance based on the owner's preferences.

[0048] In an embodiment, one of the components is the vehicle's engine. In an embodiment, one or more of the components are the vehicle's suspension. In an embodiment, one of the components is the vehicle's body.

[0049] In an embodiment, the interface for the digital twin system provides the owner with a 3D view showing a three-dimensional rendering of the vehicle model. In an embodiment, the interface for the digital twin system provides the owner with a value view to display the vehicle's condition and bluebook value based on the vehicle's state. In an embodiment, the interface for the digital twin system provides the owner with a service and maintenance view to display wear and failure of vehicle components and to predict the need for service, repair, or replacement based on the vehicle condition. In an embodiment, the vehicle operating state is the vehicle maintenance state. In an embodiment, the vehicle operating state is the vehicle energy utilization state. In an embodiment, the vehicle operating state is the vehicle navigation state. In an embodiment, the vehicle operating state is the vehicle component state. In an embodiment, the vehicle operating state is the vehicle driver state. In an embodiment, one or more inputs for the digital twin system include at least one of an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or an external system of the vehicle. In an embodiment, the digital twin system is input via an API from a vehicle edge intelligence system that provides 5G connectivity to an external system of the vehicle. In one embodiment, the digital twin system is input via an API from the vehicle's edge intelligence system, which provides internal 5G connectivity to a set of vehicle sensors and data sources.

[0050] In one embodiment, the digital twin system is input via an API from the vehicle's edge intelligence system, which provides 5G connectivity to the in-vehicle artificial intelligence system. In another embodiment, the digital twin system is automatically configured by the artificial intelligence system based on a training set of usage activities by a set of digital twin users. In yet another embodiment, the digital twin system is automatically configured by the artificial intelligence system based on a training set of usage activities by the owner.

[0051] In one embodiment, the system includes a first neural network that detects the detected satisfaction state of a user riding in a vehicle through analysis of data collected from sensors deployed in the vehicle to collect the user's physiological state, and a second neural network that optimizes the vehicle's operating parameters according to the detected satisfaction state of the user in order to achieve the user's preferred satisfaction state.

[0052] In an embodiment, the user's detected satisfaction state is the user's detected emotional state. In an embodiment, the user's preferred satisfaction state is the user's preferred emotional state. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the second neural network optimizes operating parameters based on the correlation between the vehicle operating state and the user's satisfaction state. In an embodiment, the second neural network optimizes operating parameters in real time in response to the detection of the user's detected satisfaction state by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to objects along the path, and its approach to other vehicles along the path.

[0053] In one embodiment, a system for presenting a set of vehicle operating states to a vehicle rider includes a vehicle having vehicle operating states, a digital twin system that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin system for presenting the vehicle operating states to the vehicle rider.

[0054] In one embodiment, the interface provides the rider with an optimized riding experience view, which allows the rider to select an experience mode to personalize the riding experience based on the rider's preferences.

[0055] In one embodiment, the experience mode includes a comfort mode. In another embodiment, the experience mode includes a sports mode. In another embodiment, the experience mode includes a high-efficiency mode. In another embodiment, the experience mode includes a work mode. In another embodiment, the experience mode includes an entertainment mode. In another embodiment, the experience mode includes a sleep mode. In another embodiment, the experience mode includes a relaxation mode. In another embodiment, the experience mode includes a long-distance travel mode.

[0056] In one embodiment, a system for presenting a set of vehicle operating states to a vehicle user includes a vehicle having vehicle operating states, a digital twin that receives vehicle parameter data from one or more inputs to determine the vehicle operating state, an interface for the digital twin for presenting the vehicle operating state to the vehicle user, and an identity management system that manages a set of identity and role identities of the vehicle user and determines the functions for viewing, modifying, and configuring the digital twin based on an analysis of the identity and role set.

[0057] In an embodiment, the vehicle operating state is the vehicle maintenance state. In an embodiment, the vehicle operating state is the vehicle energy utilization state. In an embodiment, the vehicle operating state is the vehicle navigation state. In an embodiment, the vehicle operating state is the vehicle component state. In an embodiment, the vehicle operating state is the vehicle driver state. In an embodiment, the input for the digital twin system includes at least one of an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or a system outside the vehicle.

[0058] In an embodiment, the system includes an identity management system for managing a set of vehicle user identities and roles. In an embodiment, the identity management system includes the ability to view, modify, and configure a digital twin system which is based on identities from a set of vehicle user identities.

[0059] In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides 5G connectivity to systems outside the vehicle. In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides internal 5G connectivity to a set of sensors and data sources within the vehicle. In an embodiment, the digital twin system is input via API from the vehicle's edge intelligence system, which provides 5G connectivity to an in-vehicle artificial intelligence system.

[0060] In one embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a set of digital twin users. In another embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a driver user. In yet another embodiment, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activities performed by a rider user.

[0061] In one embodiment, the system includes a first neural network that detects the detected satisfaction state of a rider user riding in a vehicle through analysis of data collected from sensors deployed in the vehicle to collect the rider user's physiological state, and a second neural network that optimizes the vehicle's operating parameters according to the detected satisfaction state of the rider user in order to achieve a preferred satisfaction state for the rider user.

[0062] In an embodiment, the detected satisfaction state of the rider user is the detected emotional state of the rider user. In an embodiment, the preferred satisfaction state of the rider user is the preferred emotional state of the rider user. In an embodiment, the first neural network is a recurrent neural network, and the second neural network is a radial basis function neural network. In an embodiment, at least one of the neural networks is a hybrid neural network, which includes a convolutional neural network. In an embodiment, the second neural network optimizes the operating parameters based on the correlation between the vehicle operating state and the rider user's rider satisfaction state. In an embodiment, the second neural network optimizes the operating parameters in real time in response to the detection of the rider user's detected satisfaction state by the first neural network. In an embodiment, the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to objects along the path, and its approach to other vehicles along the path.

[0063] In one embodiment, a system for presenting a set of vehicle operating states to a vehicle user includes a vehicle having vehicle operating states, a digital twin that receives vehicle parameter data from one or more inputs to determine the vehicle operating states, and an interface for the digital twin to present the vehicle operating states to the vehicle user. In one embodiment, the digital twin is linked to the user's identity so that the digital twin is automatically provided for viewing and configuration via the identified user's mobile device.

[0064] In one embodiment, a system for setting a vehicle experience based on a set of vehicle operating states and a set of rider user experience states includes a vehicle having vehicle operating states, a rider having user experience states, and a digital twin system that receives vehicle parameter data and rider user experience data from one or more inputs to determine a combined state of the vehicle and rider, and sets the vehicle experience based on the combined state.

[0065] In one embodiment, a system for setting a vehicle experience based on a set of vehicle operating states and a set of driver user experience states includes a vehicle having vehicle operating states, a driver having user experience states, and a digital twin system that receives vehicle parameter data and driver user experience data from one or more inputs to determine a combined state of the vehicle and the driver, and sets the vehicle experience based on the combined state.

[0066] In an embodiment, the vehicle operating state includes the vehicle's physical state. In an embodiment, the vehicle operating state includes the vehicle's maintenance state. In an embodiment, the vehicle operating state includes the vehicle's energy utilization state. In an embodiment, the vehicle operating state includes the vehicle's component state. In an embodiment, the vehicle operating state includes the vehicle's environmental state. In an embodiment, the driver user experience state includes the driver's physiological state. In an embodiment, the driver user experience state includes the driver's psychological state. In an embodiment, the driver user experience state includes the driver's physical state. In an embodiment, the driver user experience state includes the driver's position state. In an embodiment, the driver user experience state includes the driver's emotional state. In an embodiment, the vehicle is an autonomous vehicle.

[0067] It will be understood that any combination of features from the methods disclosed herein and / or features from the systems disclosed herein may be used together, and / or any features from any or all of these embodiments may be combined with any of the features of the embodiments and / or examples disclosed herein to achieve the advantages described herein. [Brief explanation of the drawing]

[0068] In the attached figures, similar reference figures indicate identical or functionally similar elements throughout the individual figures and are incorporated into this specification together with the following detailed description, forming part of it and helping to further describe various embodiments and to explain all the various principles and advantages according to the systems and methods disclosed herein.

[0069] [Figure 1] This perspective view shows an architecture for a transport system, illustrating specific exemplary components and arrangements related to various embodiments of the present disclosure.

[0070] [Figure 2]This is a perspective view illustrating the use of a hybrid neural network to optimize vehicle powertrain components related to various embodiments of the present disclosure.

[0071] [Figure 3] This diagram illustrates a set of states that may be provided as input to and / or controlled by an expert system / artificial intelligence (AI) system relating to various embodiments of the present disclosure.

[0072] [Figure 4] This is a diagram illustrating the range of parameters that may be taken as input by an expert system or AI system, or its components, as described through this disclosure, or that may be provided as output from such a system and / or one or more sensors, cameras, or external systems related to various embodiments of this disclosure.

[0073] [Figure 5] This is a perspective view showing a set of vehicle user interfaces related to various embodiments of the present disclosure.

[0074] [Figure 6] This is a perspective view showing a set of interfaces between transport system components related to various embodiments of the present disclosure.

[0075] [Figure 7] This perspective view illustrates a data processing system capable of processing data from various sources related to various embodiments of the present disclosure.

[0076] [Figure 8] This perspective view shows a set of algorithms that may be performed in connection with one or more of the many embodiments of the transport system described herein in relation to various embodiments of the present disclosure.

[0077] [Figure 9]This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0078] [Figure 10] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0079] [Figure 11] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0080] [Figure 12] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0081] [Figure 13] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0082] [Figure 14] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0083] [Figure 15] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0084] [Figure 16] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0085] [Figure 17] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0086] [Figure 18]This is a perspective view showing the system described throughout this disclosure in relation to various embodiments of this disclosure.

[0087] [Figure 19] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0088] [Figure 20] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0089] [Figure 21] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0090] [Figure 22] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0091] [Figure 23] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0092] [Figure 24] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0093] [Figure 25] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0094] [Figure 26] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0095] [Figure 26A]This is a perspective view showing the system described throughout this disclosure in relation to various embodiments of this disclosure.

[0096] [Figure 27] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0097] [Figure 28] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0098] [Figure 29] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0099] [Figure 30] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0100] [Figure 31] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0101] [Figure 32] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0102] [Figure 33] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0103] [Figure 34] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0104] [Figure 35]This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0105] [Figure 36] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0106] [Figure 37] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0107] [Figure 38] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0108] [Figure 39] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0109] [Figure 40] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0110] [Figure 41] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0111] [Figure 42] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0112] [Figure 43] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0113] [Figure 44]This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0114] [Figure 45] This is a perspective view illustrating the systems and methods described through this disclosure in relation to various embodiments of this disclosure.

[0115] [Figure 46] This is a perspective view illustrating the systems and methods described through this disclosure in relation to various embodiments of this disclosure.

[0116] [Figure 47] This is a perspective view illustrating the systems and methods described through this disclosure in relation to various embodiments of this disclosure.

[0117] [Figure 48] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0118] [Figure 49] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0119] [Figure 50] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0120] [Figure 51] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0121] [Figure 52] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0122] [Figure 53]This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0123] [Figure 54] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0124] [Figure 55] This is a perspective view illustrating the methods described through this disclosure relating to various embodiments of this disclosure.

[0125] [Figure 56] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0126] [Figure 57] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0127] [Figure 58] This is a perspective view illustrating the systems described through this disclosure in relation to various embodiments of this disclosure.

[0128] [Figure 59] This perspective view shows an architecture for a transportation system, including a digital twin system for vehicles, illustrating specific exemplary components and arrangements related to various embodiments of the present disclosure.

[0129] [Figure 60] This is a schematic diagram of a digital twin system integrated with an identity and access management system, according to a specific embodiment of the present disclosure.

[0130] [Figure 61] This is a schematic diagram of the interface of a digital twin system presented on a driver's user device of a vehicle relating to various embodiments of the present disclosure.

[0131] [Figure 62] This is a schematic diagram illustrating the interaction between the driver and the digital twin using one or more views and modes of the interface according to exemplary embodiments of the present disclosure.

[0132] [Figure 63] This is a schematic diagram of the interface of a digital twin system presented to a vehicle manufacturer's user device according to various embodiments of the present disclosure.

[0133] [Figure 64] In an exemplary embodiment of the present disclosure, a manufacturer uses a digital twin interface quality view to run simulations and draw up scenarios to generate what-if scenarios for vehicle quality testing.

[0134] [Figure 65] This is a schematic diagram of the interface of the digital twin system presented to the user device at the vehicle dealership.

[0135] [Figure 66] This diagram illustrates, in an exemplary embodiment, a dialogue between a dealer and a digital twin using one or more views, aimed at personalizing the customer experience when purchasing a vehicle.

[0136] [Figure 67] This figure illustrates the service and maintenance views presented to vehicle users, including drivers, vehicle manufacturers, and dealers, according to various embodiments of the present disclosure.

[0137] [Figure 68] An exemplary embodiment describes a method used by a digital twin to detect vehicle failures and predict any future failures.

[0138] [Figure 69] This is a perspective view showing a vehicle architecture having a digital twin system for predictive maintenance of the vehicle, according to an exemplary embodiment of the present disclosure.

[0139] [Figure 70] This flowchart shows a method for generating a digital twin of a vehicle according to various embodiments of the present disclosure.

[0140] [Figure 71] This perspective view shows alternative architectures for transportation systems, including vehicles and digital twin systems, according to various embodiments of the present disclosure.

[0141] [Figure 72] This figure shows a digital twin representing a combination of sets of states for both the vehicle and the vehicle's driver, according to a particular embodiment of the present disclosure.

[0142] [Figure 73] This schematic diagram illustrates a scenario in which an integrated vehicle and driver digital twin can constitute the vehicle experience, according to an exemplary embodiment.

[0143] [Figure 74] This is a schematic diagram illustrating some examples of information technology systems for traffic artificial intelligence utilizing digital twins, according to some embodiments of the present disclosure.

[0144] Those skilled in the art will understand that the elements in the figures are illustrated for simplification and clarity and are not necessarily drawn to scale. For example, the dimensions of some elements in the figures may be exaggerated relative to others to help improve the understanding of many embodiments of the systems and methods disclosed herein. [Modes for carrying out the invention]

[0145] Next, the present disclosure will be described in detail by referring to the accompanying drawings and exhibits to illustrate various exemplary, non-limiting embodiments thereof. However, the present disclosure may be embodied in many different forms and should not be construed as being limited to the exemplary embodiments described herein. Rather, the embodiments are provided to ensure that this disclosure is thorough and fully conveys the concepts of the present disclosure to those skilled in the art. To determine the true scope of the present disclosure, one should refer to the claims.

[0146] Before describing in detail embodiments of the systems and methods disclosed herein, it should be noted that embodiments primarily exist in combinations of methods and / or system components. Therefore, system components and methods are appropriately represented in the drawings by conventional symbols, showing only specific details appropriate for understanding the embodiments of the systems and methods disclosed herein.

[0147] All documents included in this book are incorporated into this book in their entirety by reference. References to singular items should be understood to include plural items unless explicitly stated otherwise or evident from the context, and vice versa. Grammatical conjunctions are intended to express any and all connecting combinations of joined clauses, sentences, words, etc., unless otherwise specified or evident from the context. Thus, the term “or” should generally be understood to mean “and / or,” etc., unless otherwise clearly evident from the context.

[0148] The ranges of values ​​in this specification are not intended to be limiting, but rather, unless otherwise specifically indicated herein, any and all values ​​falling within the range are to be referred to individually, and each individual value within such range is incorporated herein as if it were individually stated herein. Words such as “about,” “approximately,” etc., when used with numbers are to be interpreted as indicating a deviation that would be understood by those skilled in the art to work satisfactorily for the intended purpose. The ranges and / or numbers of numbers are provided herein as examples only and do not limit the scope of the embodiments described herein. Any and all examples or use of exemplary language (such as “for example,” “evidently,” etc.) provided herein are solely for the purpose of better illuminating the embodiments and do not constitute a limitation on the scope of the embodiments or claims. Nothing in this specification should be interpreted as indicating an element not claimed that is essential to the implementation of an embodiment.

[0149] In the following explanation, terms such as "first," "second," "third," "upper," and "lower" are for convenience only and should not be interpreted as referring to a chronological order or restricting any corresponding element unless explicitly stated otherwise. The term "set" should be understood to encompass a single component or a set containing multiple components.

[0150] Referring to Figure 1, an architecture for the transport system 111 is depicted, showing specific exemplary components and arrangements related to the particular embodiment described herein. The transport system 111 may include one or more vehicles 110, which include a variety of mechanical, electrical, and software components and systems such as a powertrain 113, a suspension system 117, a steering system, a braking system, a fuel system, a charging system, seats 128, a combustion engine, an electric vehicle drivetrain, a transmission 119, and a gear set. The vehicles may have a vehicle user interface 123, which may include a set of interfaces, such as a steering system, buttons, levers, a touchscreen interface, and an audio interface, as described throughout this disclosure. The vehicles may have a set of sensors 125 (including a camera 127), such as to provide input to expert system / artificial intelligence functions described throughout this disclosure, such as one or more neural networks (which may include a hybrid neural network 147 described herein). Sensors 125 and / or external information may also be communicated to an expert system / artificial intelligence (AI) system 136 to indicate or track one or more vehicle states 144, such as the vehicle operating state 345 (Figure 3), user experience state 346 (Figure 3), and others, as described herein, which may be taken as inputs to or outputs from a set of expert system / AI components. Routing information 143 may provide information to and take inputs from the expert system / AI system 136, including the use of in-vehicle navigation functions and external navigation functions, such as GPS (Global Position System), triangulation routing (e.g., cell tower), and peer-to-peer routing with other vehicles 121. The collaboration engine 129 may facilitate collaboration between vehicles and / or between vehicle users for purposes such as managing collective experience and managing a fleet. Vehicles 110 may be networked with each other in a peer-to-peer manner, such as using cognitive radio, cellular, wireless, or other networking functions.The AI ​​system 136 or other expert system may receive a wide range of vehicle parameters 130 as input, such as from an onboard diagnostic system, a telemetry system, and other software systems, as well as from sensors 125 located in the vehicle, and from external systems. In embodiments, the system may manage a set of feedback / rewards 148, incentives, etc., such as to induce specific user actions and / or to provide feedback to the AI ​​system 136 for learning about a set of results for achieving a given task or objective. The expert system or AI system 136 may notify, use, manage, or extract outputs from a set of algorithms 149, including a wide variety of those described herein. In the embodiment of the disclosure depicted in Figure 1, a data processing system 162 is connected to the hybrid neural network 147. The data processing system 162 may process data from various sources (see Figure 7). In the embodiment of the disclosure depicted in Figure 1, a system user interface 163 is connected to the hybrid neural network 147. For further disclosures relating to the interface, see the following disclosures related to Figure 6. Figure 1 shows that the area around the vehicle 164 may be part of the traffic system 111. The area around the vehicle may include roads, weather conditions, lighting conditions, etc. Figure 1 also shows that devices 165, such as mobile phones and computer systems, navigation systems, etc., may be connected to various elements of the traffic system 111 and therefore may be part of the traffic system 111 of this disclosure.

[0151] Referring to Figure 2, the herein provides a transport system having a hybrid neural network 247 for optimizing a vehicle's powertrain 213, wherein at least two parts of the hybrid neural network 247 optimize different parts of the powertrain 213. An artificial intelligence system may control the powertrain components 215 based on an operating model (such as a physical model for energy conversion, an electrodynamic model, a hydrodynamic model, a chemical model, etc., as well as a mechanical model for the operation of various dynamically interacting system components). For example, the AI ​​system may control the powertrain components 215 by manipulating powertrain operating parameters 260 to achieve a powertrain state 261. The AI ​​system may be trained to operate the powertrain components 215 by training on a resulting dataset (e.g., fuel efficiency, safety, rider satisfaction, etc.) and / or on a dataset of operator behavior (e.g., driver behavior sensed by a sensor set, cameras, etc., or a vehicle information system). In embodiments, a hybrid approach may be used in which one neural network optimizes one part of the powertrain (e.g., for gear shift operation) and another neural network optimizes another part (e.g., brakes, clutch engagement, or energy discharge and charging). Any of the powertrain components described throughout this disclosure can be controlled by a set of control commands consisting of outputs from at least one component of the hybrid neural network 247.

[0152] Figure 3 shows a set of states that are provided as input to and / or controlled by the expert system / AI system 336, and which may be used in conjunction with various systems and components in various embodiments described herein. States 344 may include vehicle operating states 345, which include vehicle configuration states, component states, diagnostic states, performance states, location states, maintenance states, and many others, as well as user experience states 346, which include experience-specific states, user emotional states 366, satisfaction states 367, location states, content / entertainment states, and many others.

[0153] Figure 4 illustrates a range of parameters 430 that may be taken as input by an expert system or AI system 136 (Figure 1), or its components, as described throughout this disclosure, or that may be provided as output from such system and / or one or more sensors 125 (Figure 1), cameras 127 (Figure 1), or external systems. Parameters 430 may include one or more targets 431 or objectives (such as those to be optimized by the expert system / AI system, including iteration and / or machine learning), such as performance targets 433 relating to fuel efficiency, travel time, satisfaction, financial efficiency, and safety. Parameters 430 may include market feedback parameters 435 relating to price, availability, location, etc., of goods, services, fuel, electricity, advertising, content, etc. Parameters 430 may include rider state parameters 437, such as parameters relating to comfort 439, emotional state, satisfaction, goals, type of travel, fatigue, etc. Parameter 430 may include parameters for various traffic-related profiles, such as traffic profiles 440 (many such as location, direction, density, and pattern over time), road profiles 441 (many such as elevation, curvature, direction, and road surface conditions), and user profiles. Parameter 430 may also include routing parameters 442, such as current vehicle location, destination, waypoints, points of interest, type of trip, travel goals, required arrival time, desired user experience, and many others. Parameter 430 may also include satisfaction parameters 443 for riders (including drivers), fleet managers, advertisers, merchants, owners, operators, insurance companies, regulators, and others. Parameter 430 may also include operational parameters 444, which include a wide variety of those described throughout this disclosure.

[0154] Figure 5 illustrates a set of vehicle user interfaces 523. The vehicle user interface 523 may include electromechanical interfaces 568 such as a steering interface, brake interface, seats, windows, moonroof, glove box, and other interfaces. Interface 523 may also include various software interfaces (which may have touch panels, dials, knobs, buttons, icons, or other functions) such as a game interface 569, a navigation interface 570, an entertainment interface 571, a vehicle settings interface 572, a search interface 573, and an e-commerce interface 574. The vehicle interface may be used to provide input to, or be controlled by, one or more AI systems / expert systems as described in embodiments throughout this disclosure.

[0155] Figure 6 shows a set of interfaces between transport system components, including interfaces within the host system (such as managing vehicles or fleets of vehicles) and host interfaces 650 between the host system and one or more third-party and / or external systems. The interfaces also include third-party interfaces 655 and end-user interfaces 651 for users of the host system, including an in-vehicle interface that may be used by LiDAR as noted in relation to Figure 5, as well as user interfaces for others such as fleet managers, insurance companies, regulators, police, advertisers, merchants, content providers, and many others. The interfaces may also include merchant interfaces 652, where a merchant may offer advertisements, content related to offerings, and one or more rewards to induce routing or other actions on the user's side. The interfaces may also include machine interfaces 653 such as application programming interfaces (APIs) 654, networking interfaces, peer-to-peer interfaces, connectors, brokers, extract-transform-load (ETL) systems, bridges, gateways, and ports. The interface may include one or more host interfaces on which the host can manage and / or configure one or more of the many embodiments described herein, such as configuring neural network components, setting weights for models, setting one or more goals or objectives, and setting reward parameters 656. The interface may include an expert system / AI system configuration interface 657 for selecting one or more models 658, selecting and configuring datasets 659 (such as sensor data, external data, and other inputs described herein), AI selection 660 and AI configuration 661 (such as selecting neural network categories and parameter weighting), expert system / AI system feedback selection 662 for learning, and supervision configuration 663, and many others.

[0156] Figure 7 shows a data processing system 758 that can process data from various sources, including social media data source 769, weather data source 770, road profile source 771, traffic data source 772, media data source 773, sensor set 774, and many others. The data processing system may be configured to extract data, convert data into a suitable format (such as for use by an interface system, an AI system / expert system, or other systems), load it into a suitable location, normalize data, clean data, dedup data, store data (such as enabling queries), and perform a wide range of processing tasks as described throughout this disclosure.

[0157] Figure 8 illustrates a set of algorithms 849 that may be performed in connection with one or more of the many embodiments of the transport systems described through this disclosure. Algorithms 849 may take input from and provide output to a set of AI systems / expert systems, such as many of the types described herein, and be managed by them. Algorithms 849 may include one or more genetic algorithms 875, such as an algorithm for providing or managing user satisfaction 874, or for determining preferred states, parameters, or combinations of states / parameters in connection with the optimization of one or more of the systems described herein. Algorithms 849 may include a vehicle routing algorithm 876, which may be sensitive to various vehicle operation parameters, user experience parameters, or other states, parameters, profiles, etc., described herein, and various goals or objectives. Algorithms 849 may include an object detection algorithm 876. Algorithms 849 may include an energy calculation algorithm 877, such as for calculating energy parameters, for optimizing fuel usage, electricity usage, etc., or for optimizing refueling or recharging time, location, amount, etc. The algorithm may include prediction algorithms such as traffic prediction algorithm 879, traffic prediction algorithm 880, and algorithms for predicting other states or parameters of a traffic system as described throughout this disclosure.

[0158] In various embodiments, the transport system 111 described herein may include vehicles (including fleets and sets of other vehicles), as well as various infrastructure systems. The infrastructure systems may include Internet of Things systems (e.g., using traffic signals, utility poles, toll booths, signs and other roadside devices and systems located on or within roads, cameras and other sensors on or inside buildings, etc.), refueling and charging systems (service stations, charging stations, etc., and wireless charging systems using wireless power transfer, etc.) and many others.

[0159] The electrical, mechanical, and / or powertrain components of a vehicle described herein may include a wide range of systems, including transmissions, gear systems, clutch systems, brake systems, fuel systems, lubrication systems, steering systems, suspension systems, lighting systems (including emergency lighting as well as interior and exterior lighting), electrical systems, and various subsystems and components thereof.

[0160] The operating state and parameters of a vehicle may include the route, purpose of the trip, geolocation, direction, vehicle range, powertrain parameters, current gear, speed / acceleration, suspension profile (including various parameters such as each wheel), charge status of electric and hybrid vehicles, fuel status of fuel-powered vehicles, and many other things described throughout this disclosure.

[0161] The Rider and / or user experience states and parameters described through this disclosure may include emotional states, comfort states, psychological states (e.g., anxiety, tension, relaxation), wakefulness / sleep states, and / or states relating to satisfaction, alertness, health, wellness, one or more goals or objectives, and many other things. User experience parameters described herein may further include those relating to driving, braking, curve approach, seat position, window status, ventilation system, climate control, temperature, humidity, sound level, entertainment content type (e.g., news, music, sports, comedy, etc.), route selection (POI, scenery, new sites, etc.), and many other things.

[0162] In the embodiment, the route may be assigned various value parameters, such as value parameters that can be optimized to improve user experience or other factors under the control of an AI system / expert system. The parameters of a route's value may include speed, duration, on-time arrival, length (e.g., in miles), objectives (e.g., seeing a Point of Interest (POI), completing a task (e.g., completing a shopping list, completing a delivery schedule, completing a meeting), refueling or charging parameters, and game-based objectives. As one of many examples, a route can be assigned a value for task completion in a model and / or as input or feedback to an AI system or expert system configured to optimize the route. A user might indicate an objective, for example, to meet at least one of a set of friends over the weekend by interacting with a user interface or menu that allows the user to set objectives. The route may be configured to increase the likelihood of a meeting (including through interaction with a system that includes location information of other vehicles and / or inputs that give an awareness of the friend's location, such as a social data feed) by intersecting with the predicted location of the friend (which may be predicted by a neural network or other AI system / expert system, as described throughout this disclosure) and by providing an in-vehicle message (or message to a mobile device) indicating the likelihood of a meeting.

[0163] Market feedback factors may be used to optimize various elements of a transport system as described through this disclosure, such as current and projected prices and / or costs (e.g., fuel, electricity, etc., and goods, services, and content available along the route and / or in the vehicle), current and projected capacity, supply and / or demand for one or more transport-related elements (fuel, electricity, charging capacity, maintenance, services, replacement parts, new or used vehicles, ride-sharing capacity, autonomous vehicle capacity or availability, etc.), and many others.

[0164] The in-vehicle or on-vehicle interface may include negotiation systems such as bidding systems, price negotiation systems, and reward negotiation systems. For example, a user may negotiate for a higher reward in exchange for agreeing to reroute to a merchant's location, or a user may stipulate a price they are willing to pay for fuel (which may be offered to a nearby refueling station that can meet that price), or similar. The output from the negotiation (agreement price, travel time, etc.) may automatically result in route reconfiguration, such as being governed by an AI system / expert system.

[0165] Rewards as described herein, particularly those offered by merchants or hosts, may include one or more coupons redeemable at a location, the granting of higher priority (such as in group routing of multiple vehicles), permission to use "high-speed lanes," priority in charging or refueling capabilities, and many others. Actions that lead to rewards in a vehicle may include playing games, downloading apps, driving to a location, taking pictures of a location or object, visiting a website, watching or listening to advertisements, watching videos, and many others.

[0166] In embodiments, the AI ​​system / expert system may use or optimize one or more parameters for a charging plan, such as for charging the battery of an electric vehicle or a hybrid vehicle. The parameters for the charging plan may include the route (e.g., route to a charging location), the amount of charge or fuel to be provided, the time for charging, the battery status, the battery charging profile, the time required for charging, the value of the charge, a value indicator, the market price, bids for charging, available supply capacity (e.g., within a geofence or range of a vehicle cluster), demand (e.g., based on the detected charging / refueling status, based on the requested demand), supply, and others. Neural networks or other systems (optionally, the hybrid systems described herein) using models or algorithms (e.g., genetic algorithms) may be used (trained over a series of trials on results and / or using a training set of human-created or human-supervised inputs) to provide a preferred and / or optimal charging plan for a vehicle or set of vehicles based on the parameters. Other inputs may include priorities for specific vehicles (e.g., for emergency responders or for persons prioritized in relation to the various embodiments described herein).

[0167] In embodiments, processors as described herein may include neural processing chips, such as those employing a fabric, such as a lambda fabric. Such chips may have multiple cores, such as 256, each core configured to be neuronal with other cores on the same chip. Each core may constitute a microscale digital signal processor, and the fabric can enable cores to easily connect to other cores on the chip. In embodiments, the fabric may connect a large number of cores (e.g., more than 500,000 cores) and / or chips, thereby facilitating use in computing environments requiring, for example, large neural networks, massively parallel computing, and large-scale, complex conditional logic. In embodiments, low-latency fabrics are used, such as those with device-to-device, rack-to-rack, or other latency of 400 nanoseconds, 300 nanoseconds, 200 nanoseconds, 100 nanoseconds, or less. The chips may be low-power chips that can be powered by energy harvesting from the environment, energy harvesting from test signals, energy harvesting from onboard antennas, etc. In embodiments, the core may be configured to enable the application of a set of sparse matrix heterogeneous machine learning algorithms. The chip may execute an object-oriented programming language such as C++ or Java. In embodiments, the chip may be programmed to run each core on a different algorithm, thereby enabling algorithmic heterogeneity to enable one or more embodiments of the hybrid neural network described through this disclosure. The chip can thereby take multiple inputs from multiple data sources (e.g., one per core), perform massively parallel processing using a large set of different algorithms, and provide multiple outputs (such as one per core or one per set of cores).

[0168] In some embodiments, the chip may include, or enable, a security fabric, such as a fabric for performing content inspection, packet inspection (against blacklists, whitelists, etc.), in addition to undertaking processing tasks such as neural networks and hybrid AI solutions.

[0169] In embodiments, the platform described herein may include, and may be integrated with or connected to, a system for robotic process automation (RPA), thereby training an artificial intelligence / machine learning system with a training set of data consisting of tracking and recording a set of human interactions as a human interacts with a set of interfaces, for example, a graphical user interface (e.g., mouse, trackpad, keyboard, touchscreen, joystick, interaction with remote control devices), an audio system interface (e.g., via microphone, smart speaker, voice response interface, intelligent agent interface (e.g., Siri and Alexa)), a human-machine interface (robot system, prosthetics, cybernetic system, exoskeleton system, wearable (clothing, headgear, headphones, watch, wristband, glasses, armband, torso band, etc.) These include belts, rings, necklaces and other accessories), physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touchscreens, levers, handles, steering systems, wheels, etc.), optical interfaces (including those triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, etc.), and sensor-enabled interfaces (including cameras, EEG or other electrical signal sensing (for brain-computer interfaces, etc.), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR, and other sensor sets capable of recognizing thoughts, gestures (face, hands, posture, etc.), speech, etc.). In addition to tracking and recording human interactions, RPA systems can also track and record a series of states, actions, events, and results that occur within, from, or about systems and processes in which humans are involved.For example, an RPA system may record mouse clicks on video frames that occur during a human review of a video, such as highlighting points of interest in the video, tagging objects in the video, retrieving parameters (size, dimensions, etc.), or performing other operations on the video within a graphical user interface. The RPA system can also record the state and events of the system or process, for example, which elements were the subject of the interaction, what the system's state was before, during, and after the interaction, and what outputs were provided or what results were achieved by the system. Through a large training set of observing human interactions and system states, events, and results, an RPA system can learn to interact with the system in a way that mimics that of a human. Learning may be enhanced by training and monitoring, such as when a human corrects the RPA system as it attempts to perform actions that a human would have done (e.g., tagging the correct object, correctly labeling an item, selecting the correct button to initiate the next step in a process), and the RPA system can become increasingly effective at replicating actions that a human would have done during a series of trials. Learning may include deep learning, such as by reinforcing learning based on successful outcomes (e.g., based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, the RPA system may be seeded with a set of expert human interactions during the learning phase so that the RPA system begins to be able to replicate expert interactions with the system. For example, an expert driver's interaction with a robotic system such as a remotely controlled vehicle or UAV may be recorded along with information about the vehicle's state (e.g., surrounding environment, navigation parameters, and purpose) so that the RPA system can learn to drive the vehicle in a way that reflects the same choices as the expert driver.After being instructed to replicate the human skills or expertise of an expert, the RPA system may transition to a deep learning mode, where it further improves based on a series of results, such as being configured to try some level of variation in its approach (for example, tracking results (with feedback) so that the RPA system can learn to surpass the expertise of a human expert through variation / experimentation (which may be randomization, rule-based, etc., such as using genetic programming techniques, random walk techniques, random forest techniques, etc.) and selection). Thus, the RPA system can be given a seed that is highly effective for artificial intelligence, such as providing a seed model or system that can learn from human experts, acquire expertise in interacting with systems or processes, facilitate process automation (for example, by taking over some of the more repetitive tasks, including those that require consistent execution of acquired skills), and improve through machine learning with feedback on the results of the system or process.

[0170] RPA systems can be particularly valuable in situations where human expertise or knowledge is acquired through training and experience, and where the human brain and sensory systems are particularly adapted and evolved to solve computationally difficult or highly complex problems. Therefore, in embodiments, an RPA system may be used to learn to undertake, in particular, visual pattern recognition tasks relating to various systems, processes, workflows, and environments described herein (e.g., recognizing the meaning of dynamic interactions of objects or entities in a video stream (e.g., understanding what happens when humans and objects interact in a video)), recognition of the significance of visual patterns (e.g., recognizing objects, structures, defects, and states in a photograph or X-ray image), tagging relevant objects in a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (e.g., person recognition)), displaying metrics in a visual pattern (e.g., dimensions of objects shown by clicking on dimensions in an X-ray image), and labeling activities in a visual pattern by category (e.g., what work processes are being performed). This includes, for example, the recognition of patterns that appear as signals (e.g., waves or similar patterns in the frequency domain, time domain, or other signal processing representations), the prediction of future states based on current states (e.g., predicting the movement of flying or rolling objects, predicting the next action of a human in a process, predicting the next step of a machine, predicting a human reaction to an event, and many others), the recognition and prediction of emotional states and reactions (e.g., based on facial expressions, posture, body language, etc.), the application of heuristics to achieve a desirable state without deterministic calculation (e.g., selecting a favorable strategy in sports or games, selecting a business strategy, selecting a negotiation strategy, pricing a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others), and many other things.In embodiments, an RPA system can automate workflows involving visual inspection of people, systems, and objects (including internal components); workflows involving the execution of software tasks involving sequential interaction with a series of screens in a software interface; workflows involving the remote control of robots and other systems and devices; workflows involving content creation (e.g., selection, editing, and arrangement); and many other workflows involving content creation (such as content selection, editing, and ordering); financial decision-making and negotiation (such as setting prices and conditions for financial transactions); and decision-making (such as selecting the optimal configuration of a system or subsystem, or selecting the optimal path or sequence of actions in workflows, processes, and other activities involving dynamic decision-making).

[0171] In embodiments, the RPA system may use a set of IoT devices and systems (such as cameras and sensors) to track and record human behavior and interactions with various interfaces and systems in the environment. The RPA system may also use data from onboard sensors, telemetry, and event recording systems (such as telemetry systems on vehicles and event logs on computers). Thus, the RPA system (or a set of RPA systems dedicated to automating various processes and workflows) can be trained to achieve processes and workflows in a way that reflects and mimics accumulated human expertise, and ultimately improve the results of that human expertise through further machine learning, including various entities (human and non-human), systems, processes, applications (e.g., software applications used to enable workflows), states, events, and results.

[0172] Referring to Figure 9, an embodiment provided herein is a transport system 911 having an artificial intelligence system 936 that uses at least one genetic algorithm 975 to explore a set of possible vehicle operating states 945 in order to determine at least one optimized operating state. In the embodiment, the genetic algorithm 975 takes inputs related to at least one vehicle performance parameter 982 and at least one lidar state 937.

[0173] Embodiments provided herein are transport systems 911 comprising a vehicle 910 having a vehicle operating state 945, and an artificial intelligence system 936 that executes a genetic algorithm 975 that generates mutations from an initial vehicle operating state to determine at least one optimized vehicle operating state. In embodiments, the vehicle operating state 945 includes a set of vehicle parameter values ​​984. In embodiments, the genetic algorithm 975 varies the set of vehicle parameter values ​​984 for a set of corresponding time periods such that the vehicle 910 operates according to the set of vehicle parameter values ​​984 during the corresponding time periods, evaluates the vehicle operating state 945 for each of the corresponding time periods according to a set of measures 983 to generate evaluations, and selects an optimized set of vehicle parameter values ​​for future operation of the vehicle 910 based on the evaluations.

[0174] In an embodiment, the vehicle operating state 945 includes the rider state 937 of the vehicle's rider. In an embodiment, at least one optimized vehicle operating state includes the optimized rider state. In an embodiment, the genetic algorithm 975 is to optimize the rider state. In an embodiment, evaluating according to a set of measures 983 is to determine the rider state corresponding to the vehicle parameter value 984.

[0175] In an embodiment, the vehicle operating state 945 includes the state of the vehicle's rider. In an embodiment, the vehicle parameter value set 984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the state of the rider and the state of vehicle performance. In an embodiment, evaluating according to a set of measures 983 is to determine the state of the rider and the state of vehicle performance corresponding to the vehicle performance control values.

[0176] In an embodiment, the vehicle parameter value set 984 includes a vehicle performance control value set. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the state of vehicle performance. In an embodiment, evaluating according to the set of measures 983 is to determine the state of vehicle performance corresponding to the vehicle performance control values.

[0177] In an embodiment, the set of vehicle parameter values ​​984 includes rider occupancy parameter values. In an embodiment, the rider occupancy parameter values ​​affirm the presence of a rider in the vehicle 910. In an embodiment, the vehicle operating state 945 includes the rider state 937 of the vehicle's rider. In an embodiment, at least one optimized vehicle operating state includes the rider's optimized state. In an embodiment, the genetic algorithm 975 is to optimize the rider state. In an embodiment, evaluating according to the set of measures 983 is to determine the rider state corresponding to the vehicle parameter value 984. In an embodiment, the rider state includes rider satisfaction parameters. In an embodiment, the rider state includes inputs representing the rider. In an embodiment, the inputs representing the rider are selected from the group consisting of rider state parameters, rider comfort parameters, rider emotional state parameters, rider satisfaction parameters, rider goal parameters, travel classifications, and combinations thereof.

[0178] In an embodiment, the vehicle parameter value set 984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the rider state and the vehicle performance state. In an embodiment, evaluation according to the set of measures 983 is to determine the rider state and the vehicle performance state corresponding to the vehicle performance control values. In an embodiment, the vehicle parameter value set 984 includes a set of vehicle performance control values. In an embodiment, at least one optimized vehicle operating state includes an optimized state of vehicle performance. In an embodiment, the genetic algorithm 975 is to optimize the state of vehicle performance. In an embodiment, evaluation according to the set of measures 983 is to determine the vehicle performance state corresponding to the vehicle performance control values.

[0179] In embodiments, the set of vehicle performance control values ​​is selected from the group consisting of fuel efficiency, travel duration, vehicle wear, vehicle manufacturer, vehicle model, vehicle energy consumption profile, fuel capacity, real-time fuel level, charge capacity, charge capability, regenerative braking state, and combinations thereof. In embodiments, at least a portion of the set of vehicle performance control values ​​is supplied from at least one of an onboard diagnostic system, a telemetry system, a software system, sensors placed in the vehicle, and a system outside the vehicle 910. In embodiments, the set of measures 983 relates to a set of vehicle operation criteria. In embodiments, the set of measures 983 relates to a set of rider satisfaction criteria. In embodiments, the set of measures 983 relates to a combination of vehicle operation criteria and rider satisfaction criteria. In embodiments, each evaluation uses feedback indicating an impact on at least one of the vehicle performance state and the rider state.

[0180] Embodiments provided herein are systems for transport 911, comprising an artificial intelligence system 936 that processes inputs representing the vehicle state and inputs representing the rider state 937 of a rider occupying the vehicle in a genetic algorithm 975 to optimize a set of vehicle parameters that affect the vehicle state or rider state 937. In embodiments, the genetic algorithm 975 is to perform a series of evaluations using variations of the inputs. In embodiments, each evaluation in the series of evaluations uses feedback indicating an effect on at least one of the vehicle operating state 945 and the rider state 937. In embodiments, the input representing the rider state 937 indicates that the rider is absent from the vehicle 910. In embodiments, the vehicle state includes the vehicle operating state 945. In embodiments, the vehicle parameters in the set of vehicle parameters include the vehicle performance parameter 982. In embodiments, the genetic algorithm 975 is to optimize a set of vehicle parameters for the rider state.

[0181] In embodiments, optimizing a set of vehicle parameters is a response to the genetic algorithm 975 identifying at least one vehicle parameter that results in a preferred rider state. In embodiments, the genetic algorithm 975 optimizes the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm 975 optimizes the set of vehicle parameters for rider states and optimizes the set of vehicle parameters for vehicle performance. In embodiments, optimizing a set of vehicle parameters is a response to the genetic algorithm 975 identifying at least one of a preferred vehicle operating state and a preferred vehicle performance that maintains a rider state 937. In embodiments, the artificial intelligence system 936 further includes a neural network selected from a plurality of different neural networks. In embodiments, the selection of the neural network includes the genetic algorithm 975. In embodiments, the selection of the neural network is based on structured competition among a plurality of different neural networks. In embodiments, the genetic algorithm 975 facilitates training a neural network to process interactions between a plurality of vehicle operating systems and riders to generate an optimized set of vehicle parameters.

[0182] In embodiments, a set of inputs relating to at least one vehicle parameter is provided by at least one of an onboard diagnostic system, a telemetry system, a sensor located in the vehicle, and an external system. In embodiments, the input representing the rider state 937 consists of at least one of comfort, emotional state, satisfaction, goals, travel classification, or fatigue. In embodiments, the input representing the rider state 937 reflects at least one satisfaction parameter from among the driver, fleet manager, advertiser, merchant, owner, operator, insurance company, and regulator. In embodiments, the input representing the rider state 937 consists of user-related inputs that, when processed by a cognitive system, result in the rider state 937.

[0183] Referring to Figure 10, an embodiment provided herein provides a transport system 1011 having a hybrid neural network 1047 for optimizing the operating state of a continuously variable powertrain 1013 of a vehicle 1010. In an embodiment, at least a portion of the hybrid neural network 1047 operates to classify the state of the vehicle 1010, and another portion of the hybrid neural network 1047 operates to optimize at least one operating parameter 1060 of the transmission 1019. In an embodiment, the vehicle 1010 may be an autonomous vehicle. For example, the first part 1085 of the hybrid neural network may classify the vehicle 1010 into high-traffic conditions (e.g., by using LIDAR, RADAR, etc., to indicate the presence of other vehicles, or by acquiring input from a traffic monitoring system, or by detecting the presence of a high density of mobile devices, etc.), adverse weather conditions (e.g., by taking input indicating wet roads (e.g., using a visual-based system), precipitation (e.g., determined by radar), presence of ice (e.g., temperature sensing, visual-based sensing), hail (e.g., impact detection, sound sensing), lightning (e.g., a visual-based system, a sound-based system, etc.), etc.). Once classified, another neural network 1086 (optionally of a different type) may optimize vehicle operating parameters based on the classified conditions, such as putting the vehicle 1010 into a safe driving mode (e.g., by providing forward-sensing warnings at a greater distance and / or lower speed than in good weather, by providing automatic braking earlier and more aggressively than in good weather, etc.).

[0184] Embodiments provided herein are systems 1011 for transport, comprising a hybrid neural network 1047 for optimizing the operating states of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, a portion 1085 of the hybrid neural network 1047 operates to classify states 1044 of the vehicle 1010 and thereby generate classified states of the vehicle, and another portion 1086 of the hybrid neural network 1047 operates to optimize at least one operating parameter 1060 of the transmission portion 1019 of the continuously variable powertrain 1013.

[0185] In an embodiment, the transport system 1011 further comprises an artificial intelligence system 1036 operating on at least one processor 1088, an artificial intelligence system 1036 operating a portion 1085 of a hybrid neural network 1047 that operates to classify the state of the vehicle, and an artificial intelligence system 1036 operating another portion 1086 of the hybrid neural network 1047 to optimize at least one operating parameter 1087 of the transmission portion 1019 of the continuously variable powertrain 1013 based on the classified state of the vehicle. In an embodiment, the vehicle 1010 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 1010 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 1010 is automatically routed. In an embodiment, the vehicle 1010 is an autonomous driving vehicle. In the embodiment, the classified states of the vehicle are vehicle maintenance state, vehicle health state, vehicle operation state, vehicle energy utilization state, vehicle charging state, vehicle satisfaction state, vehicle component state, vehicle subsystem state, vehicle powertrain system state, vehicle brake system state, vehicle clutch system state, vehicle lubrication system state, vehicle traffic infrastructure system state, or vehicle occupant state. In the embodiment, at least a portion of the hybrid neural network 1047 is a convolutional neural network.

[0186] Figure 11 shows a method 1100 for optimizing the operation of a continuously variable vehicle powertrain of a vehicle, according to embodiments of the systems and methods disclosed herein. In 1102, the method comprises running a first network of a hybrid neural network on at least one processor, the first network classifying a plurality of operating states of the vehicle. In embodiments, at least some of the operating states are based on the state of the vehicle's continuously variable powertrain. In 1104, the method comprises running a second network of a hybrid neural network on at least one processor, the second network processing inputs describing at least one detected state related to the vehicle and the vehicle's occupants for at least one of the plurality of classified driving states of the vehicle. In embodiments, the processing of inputs by the second network results in the optimization of at least one driving parameter of the vehicle's continuously variable powertrain for the plurality of driving states of the vehicle.

[0187] Referring together to Figures 10 and 11, in an embodiment, the vehicle is equipped with an artificial intelligence system 1036, and the method further includes automating at least one control parameter of the vehicle by the artificial intelligence system 1036. In an embodiment, the vehicle 1010 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 1010 is automatically routed. In an embodiment, the vehicle 1010 is an autonomous vehicle. In an embodiment, the method further includes optimizing the operating state of the vehicle's continuously variable powertrain 1013 based on an optimized at least one operating parameter 1060 of the continuously variable powertrain 1013 by adjusting at least one other operating parameter 1087 of the transmission portion 1019 of the continuously variable powertrain 1013 by the artificial intelligence system 1036.

[0188] In embodiments, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing social data from multiple social data sources using an artificial intelligence system 1036. In embodiments, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a stream of data from an unstructured data source using an artificial intelligence system 1036. In embodiments, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a wearable device using an artificial intelligence system 1036. In embodiments, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from an in-vehicle sensor using an artificial intelligence system 1036. In embodiments, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from a rider's helmet using an artificial intelligence system 1036.

[0189] In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from the rider headgear using an artificial intelligence system 1036. In an embodiment, the method further comprises optimizing the operating state of the continuously variable powertrain 1013 by processing data supplied from the rider voice system using an artificial intelligence system 1036. In an embodiment, the method further comprises using an artificial intelligence system 1036 to operate a third network of the hybrid neural network 1047 to predict the state of the vehicle at least in part on at least one of a plurality of classified operating states of the vehicle and at least one operating parameter of the transmission 1019. In an embodiment, the first network of the hybrid neural network 1047 constitutes a structure-adaptive network for adapting the structure of the first network in response to the results of operating the first network of the hybrid neural network 1047. In an embodiment, the first network of the hybrid neural network 1047 processes a plurality of social data from a social data source to classify a plurality of operating states of the vehicle.

[0190] In an embodiment, at least a portion of the hybrid neural network 1047 is a convolutional neural network. In an embodiment, at least one of the classified operating states of the vehicle is a vehicle maintenance state or a vehicle health state. In an embodiment, at least one of the classified states of the vehicle is a vehicle operating state, a vehicle energy utilization state, a vehicle charging state, a vehicle satisfaction state, a vehicle component state, a vehicle subsystem state, a vehicle powertrain system state, a vehicle brake system state, a vehicle clutch system state, a vehicle lubrication system state, or a vehicle traffic infrastructure system state. In an embodiment, at least one of the classified states of the vehicle is a vehicle driver state. In an embodiment, at least one of the classified states of the vehicle is a vehicle rider state.

[0191] Referring to Figure 12, in an embodiment, provided herein is a transport system 1211 having a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on route parameters determined by facilitating negotiation between a given set of vehicles. In an embodiment, the negotiation accepts input regarding the value that at least one rider attributes to at least one parameter 1230 of the route 1295. A user 1290 may express value by an action (e.g., undertaking an action that reflects or indicates a value attributable to arriving on time, following a given route 1295, etc.) or by providing or offering value (e.g., providing currency, tokens, points, cryptocurrency, rewards, etc.) through a user interface that evaluates one or more parameters (e.g., any of the parameters pointed out throughout). For example, user 1290 may negotiate a preferred route by offering the system a token that will be given if user 1290 arrives at a specified time, while another may offer to accept the token in exchange for taking an alternative route (thus reducing congestion). Therefore, the artificial intelligence system may optimize the combination of offers to provide rewards or to undertake actions in response to rewards so that the reward system optimizes a set of outcomes. Negotiations may include explicit negotiations, for example, in which a driver offers to give a reward to a driver ahead of them on the road in exchange for temporarily leaving the route as the driver passes.

[0192] Embodiments provided herein are a system 1211 for transport, a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on routing parameters determined by facilitating negotiation between a designated set of vehicles, wherein the negotiation receives input relating to a value at least one user 1290 attributable to at least one parameter of a route 1295.

[0193] Figure 13 shows a negotiation-based vehicle routing method 1300 according to embodiments of the systems and methods disclosed herein. In 1302, the method includes facilitating the negotiation of route adjustment values ​​for a plurality of parameters used by a vehicle routing system to route at least one vehicle in a set of vehicles. In 1304, the method includes determining parameters in a plurality of parameters to optimize at least one result based on the negotiation.

[0194] Referring to Figures 12 and 13, in an embodiment, user 1290 is the administrator for a set of roads used by at least one vehicle 1210 in a set of vehicles 1294. In an embodiment, user 1290 is the administrator for a fleet of vehicles including the set of vehicles 1294. In an embodiment, the method further includes providing user 1290 with respect to the set of vehicles 1294 a set of provided user-indicated values ​​for a plurality of parameters 1230. In an embodiment, the route adjustment value 1224 is at least partially based on the provided set of user-indicated values ​​1297. In an embodiment, the route adjustment value 1224 is further based on at least one user response to the provision. In an embodiment, the route adjustment value 1224 is at least partially based on the provided set of user-indicated values ​​1297 and at least one response to it by at least one user of the set of vehicles 1294. In an embodiment, the determined parameters facilitate the adjustment of at least one route 1295 for vehicle 1210 in the set of vehicles 1294. In an embodiment, route adjustment includes prioritizing parameters determined for use by a vehicle routing system.

[0195] In an embodiment, facilitating negotiation includes facilitating negotiation on the price of a service. In an embodiment, facilitating negotiation includes facilitating negotiation on the price of fuel. In an embodiment, facilitating negotiation includes facilitating negotiation on the price of charging. In an embodiment, facilitating negotiation includes facilitating negotiation on a reward for taking routing action.

[0196] Embodiments provided herein include a transport system 1211 for negotiation-based vehicle routing. It comprises a route adjustment negotiation system 1289 in which a user 1290 of a set of users 1291 negotiates a route adjustment value 1224 for at least one of a plurality of parameters 1230 used by a vehicle routing system 1292 to route at least one vehicle 1210 from a set of vehicles 1294, and a user route optimization circuit 1293 that optimizes a portion of the route 1295 for at least one user 1290 of the set of vehicles 1294 based on the route adjustment value 1224 for at least one of the plurality of parameters 1230. In embodiments, the route adjustment value 1224 is at least partially based on a user instruction value 1297 and at least one negotiation response thereto by at least one user of the set of vehicles 1294. In embodiments, the transport system 1211 further comprises a vehicle-based route negotiation interface into which the user instruction value 1297 for the plurality of parameters 1230 used by the vehicle routing system is taken up. In one embodiment, user 1290 is the rider of at least one vehicle 1210. In another embodiment, user 1290 is the administrator of a set of roads 1294 used by at least one vehicle 1210.

[0197] In an embodiment, user 1290 is an administrator of a fleet of vehicles including a set of vehicles 1294. In an embodiment, at least one of a plurality of parameters 1230 facilitates the coordination of a route 1295 for at least one vehicle 1210. In an embodiment, coordinating a route 1295 involves prioritizing parameters determined for use by the vehicle routing system. In an embodiment, at least one user-indicated value 1297 is reduced to at least one of the plurality of parameters 1230 via an interface to facilitate the expression of an evaluation of one or more route parameters. In an embodiment, a vehicle-based route negotiation interface facilitates the expression of a ranking of one or more route parameters. In an embodiment, the user-indicated value 1297 is derived from the actions of user 1290. In an embodiment, the vehicle-based route negotiation interface facilitates the translation of user behavior into a user-indicated value 1297. In an embodiment, user behavior reflects a value assigned to at least one parameter used by the vehicle routing system to influence the route 1295 of at least one vehicle 1210 in the vehicle set 1294. In an embodiment, a user-indicated value shown by at least one user 1290 correlates to a value item provided by user 1290. In an embodiment, a value item is provided by user 1290 through the provision of a value item in exchange for the routing result based on at least one parameter. In an embodiment, negotiation of a route adjustment value 1224 includes providing a value item to users of the vehicle set 1294.

[0198] Referring to Figure 14, an embodiment provided herein provides a transport system 1411 having a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on routing parameters determined by facilitating coordination among a given set of vehicles 1498. In the embodiment, coordination is achieved by taking at least one input from at least one game-based interface 1499 for the rider of a vehicle. The game-based interface 1499 may include rewards for undertaking gamified behaviors (i.e., game activities 14101) that provide an additional benefit. For example, the rider of vehicle 1410 may be rewarded for routing vehicle 1410 to a point of interest off the highway (such as collecting coins or capturing items), and the rider's departure would free up space for other vehicles attempting to achieve other objectives, such as on-time arrival. For example, a game like Pokémon Go® may be configured to indicate the presence of rare Pokémon® creatures to attract traffic to locations away from congested areas. Alternatively, rewards (e.g., currency, cryptocurrency, etc.) that can be pooled to attract users away from congested roads may be offered.

[0199] Embodiments provided herein are systems 1411 for transport, comprising a cognitive system for routing at least one vehicle 1410 within a vehicle set 1494 based on a set of routing parameters 1430 determined by facilitating coordination among designated vehicle sets 1498, wherein the coordination is achieved by taking at least one input from at least one game-based interface 1499 to a user 1490 of the vehicle 1410 within the vehicle set 1498.

[0200] In an embodiment, the transport system comprises a vehicle routing system 1492 that routes at least one vehicle 1410 based on a set of routing parameters 1430, and a game-based interface 1499 that directs a user 1490 to a routing preference 14100 for at least one vehicle 1410 in a set of vehicles 1494 in order to undertake a game activity 14101 provided in the game-based interface 1499, the game-based interface 1499 being for instructing the user 1490 to undertake a set of preferred routing options based on a set of routing parameters 1430. As used herein, “to route” means to select a route 1495.

[0201] In an embodiment, the vehicle routing system 1492 takes into account the routing preference 14100 of user 1490 when routing at least one vehicle 1410 within the vehicle set 1494. In an embodiment, the game-based interface 1499 is positioned for in-vehicle use, as shown in Figure 14 by a line extending from the game-based interface into a box for vehicle 1. In an embodiment, user 1490 is the lidar of at least one vehicle 1410. In an embodiment, user 1490 is the administrator of the road set used by at least one vehicle 1410 in the vehicle set 1494. In an embodiment, user 1490 is the administrator of the vehicle fleet including the vehicle set 1494. In embodiments, the set of routing parameters 1430 includes at least one of traffic congestion, desired arrival time, preferred route, fuel efficiency, pollution reduction, accident avoidance, avoidance of bad weather, avoidance of poor road conditions, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, avoidance of high-crime areas, collective satisfaction, speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undistributed highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the game activity 14101 provided in the game-based interface 1499 includes contests. In embodiments, the game activity 14101 provided in the game-based interface 1499 includes entertainment games.

[0202] In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a competitive game. In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a strategy game. In an embodiment, the game activity 14101 provided by the game-based interface 1499 includes a scavenger hunt. In an embodiment, a set of favorable route selections configures the vehicle routing system 1492 to achieve a fuel efficiency target. In an embodiment, a set of favorable route selections configures the vehicle route control system 1492 to achieve a traffic volume reduction objective. In an embodiment, a set of favorable route selections configures the vehicle route control system 1492 to achieve a pollution reduction objective. In an embodiment, a set of favorable route selections configures the vehicle route selection system 1492 to achieve a carbon footprint reduction objective.

[0203] In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of reducing neighborhood noise. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of collective satisfaction. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding accident sites. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding high-crime areas. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of reducing traffic congestion. In embodiments, the set of favorable route selections is configured so that the vehicle routing system 1492 achieves the objective of avoiding bad weather.

[0204] In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve a maximum travel time objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve a maximum speed limit objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve a toll road avoidance objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve urban road avoidance objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve undivided highway avoidance objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve left turn avoidance objective. In an embodiment, the set of favorable route selections is configured to enable the vehicle routing system 1492 to achieve driver-operated vehicle avoidance objective.

[0205] Figure 15 shows a method 1500 of game-based cooperative vehicle routing according to embodiments of the systems and methods disclosed herein. In 1502, the method includes presenting game activities that influence vehicle route preferences in a game-based interface. In 1504, the method includes receiving user responses to the presented game activities via the game-based interface. In 1506, the method includes adjusting the user's route preferences in response to the received responses. In 1508, the method includes determining at least one vehicle routing parameter to be used to route the vehicles in order to reflect the adjusted routing preferences for routing the vehicles. In 1508, the method includes routing vehicles in a set of vehicles in a vehicle routing system in response to at least one determined vehicle routing parameter adjusted to reflect the adjusted routing preferences, wherein the vehicle routing includes adjusting the determined routing parameter for at least one vehicle in the set of vehicles.

[0206] Referring to Figures 14 and 15, in an embodiment, the method further comprises a game-based interface 1499 indicating a reward value 14102 for accepting a game activity 14101. In an embodiment, the game-based interface 1499 further comprises a routing preference negotiation system 1436 for the rider to negotiate the reward value 14102 for accepting the game activity 14101. In an embodiment, the reward value 14102 is the result of pooling value contributions from riders in a set of vehicles. In an embodiment, at least one routing parameter 1430 used by a vehicle routing system 1492 to route vehicles 1410 in a set of vehicles 1494 is associated with a game activity 14101, and user acceptance of the game activity 14101 adjusts at least one routing parameter 1430 (e.g., by a routing adjustment value 1424) to reflect routing preferences. In an embodiment, the user response to the presented game activity 14101 is obtained from user interaction with the game-based interface 1499. In an embodiment, at least one routing parameter used by the vehicle routing system 1492 to route the vehicles 1410 in the vehicle set 1494 includes at least one of the following: traffic congestion, desired arrival time, preferred route, fuel efficiency, pollution reduction, accident avoidance, avoidance of bad weather, avoidance of poor road conditions, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, avoidance of high-crime areas, crowd satisfaction, speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undistributed highways, avoidance of left turns, and avoidance of vehicles being driven.

[0207] In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a contest. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes an entertainment game. In an embodiment, the game activity 14101 presented in the game-based interface 1496 includes a competitive game. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a strategy game. In an embodiment, the game activity 14101 presented in the game-based interface 1499 includes a scavenger hunt. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a fuel efficiency target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced traffic objective.

[0208] In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced pollution target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a reduced carbon footprint target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a neighborhood noise reduction target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a collective satisfaction target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a crime avoidance target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a high crime rate avoidance target. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves a traffic congestion reduction target.

[0209] In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding bad weather. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of achieving the maximum travel time. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of achieving the maximum speed limit. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding toll roads. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding urban roads. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding undivided highways. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding left turns. In an embodiment, routing in response to at least one determined vehicle routing parameter 14103 achieves the objective of avoiding the vehicle being operated by the driver.

[0210] In embodiments, provided herein is a transport system 1611 having a cognitive system for routing at least one vehicle, the routing being at least partially determined by processing at least one input from a rider interface in which the rider can receive a reward 16102 by undertaking a certain action while riding in the vehicle. In embodiments, the rider interface may display a set of rewards to pursue, a set of rewards available for performing various actions (e.g., by interacting with a touch panel or audio interface), such as enabling the rider to use actions that result in rewards for the vehicle's navigation system (or a rideshare system at least partially controlled by a user 1690) or the routing system 1692 of an autonomous vehicle to take control of the routing. For example, selecting a reward for joining a site may result in sending a signal to the navigation or routing system 1692 to set the site as an intermediate destination. As another example, indicating an intention to view part of content may cause the routing system 1692 to select a route that allows sufficient time to view or listen to the content.

[0211] Embodiments provided herein include a cognitive system 1611 for routing at least one vehicle 1610, the routing being at least partially based on processing at least one input from a rider interface, and the reward 16102 being made available to the rider in response to the rider undertaking a predetermined action while riding in at least one vehicle 1610.

[0212] Embodiments provided herein include a transport system 1611 for reward-based cooperative vehicle routing, comprising: a reward-based interface 16104 for providing rewards 16102, through which a user 1690 associated with a vehicle set 1694 indicates the user 1690's routing preferences related to the rewards 16102 by responding to the rewards 16102 provided by the reward-based interface 16104; a reward-providing response processing circuit 16105 for determining at least one user action resulting from the user's response to the rewards 16102 and the corresponding effect 16106 on at least one routing parameter 1630; and a vehicle routing system 1692 for governing the routing of the vehicle set 1694 using the user 1690's routing preferences 16100 and the corresponding effect on at least one routing parameter.

[0213] In an embodiment, user 1690 is the rider of at least one vehicle 1610 in a set of vehicles 1694. In an embodiment, user 1690 is the administrator for a set of roads used by at least one vehicle 1610 in a set of vehicles 1694. In an embodiment, user 1690 is the administrator for a fleet of vehicles including a set of vehicles 1694. In an embodiment, the reward-based interface 16104 is configured for in-vehicle use. In an embodiment, at least one routing parameter 1630 includes at least one of the following: congestion, desired arrival time, priority route, fuel efficiency, pollution reduction, accident avoidance, bad weather avoidance, bad road conditions avoidance, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, high-crime area avoidance, collective satisfaction, speed limit, toll road avoidance, city road avoidance, undistributed highway avoidance, left turn avoidance, and avoidance of driver-operated vehicles. In an embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve a fuel efficiency target using the user 1690's routing preference and the corresponding effect on at least one routing parameter. In an embodiment, the vehicle route control system 1692 controls the route of a set of vehicles to achieve a traffic volume reduction objective using the user 1690's route control preference and the corresponding effect on at least one route control parameter. In an embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve a pollution reduction objective using the user 1690's routing preference and the corresponding effect on at least one routing parameter. In an embodiment, the vehicle route control system 1692 controls the route of a set of vehicles to achieve a carbon footprint reduction objective using the user 1690's route selection and the corresponding effect on at least one route control parameter.

[0214] In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to control the routing of a vehicle group in order to achieve the objective of reducing neighborhood noise. In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and corresponding effects for at least one route control parameter to control the route control of a vehicle set in order to achieve the objective of collective satisfaction. In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and corresponding effects for at least one route control parameter to control the route of a vehicle group in order to achieve the objective of avoiding accident sites. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to control the routing of a vehicle set in order to achieve the objective of avoiding high-crime areas. In one embodiment, the vehicle routing system 1692 controls the routing of a set of vehicles to achieve the objective of reducing traffic congestion, using the routing preferences of the user 1690 and the corresponding effects on at least one routing parameter.

[0215] In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and the corresponding effects for at least one route control parameter to control the route of a set of vehicles in order to achieve the objective of avoiding bad weather. In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and the corresponding effects for at least one route control parameter to control the route of a set of vehicles in order to achieve the objective of achieving maximum travel time. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and the corresponding effects for at least one routing parameter to control the routing of a set of vehicles in order to achieve the objective of achieving maximum speed limit. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and the corresponding effects for at least one routing parameter to control the routing of a set of vehicles in order to achieve the objective of avoiding toll roads. In one embodiment, the vehicle routing system 1692 uses the routing preferences of the user 1690 and the corresponding effects on at least one routing parameter to control the routing of a set of vehicles in order to achieve the objective of avoiding urban roads.

[0216] In an embodiment, the vehicle route control system 1692 uses the user 1690's route control preferences and corresponding effects for at least one route control parameter to govern the route control of a set of vehicles in order to achieve the objective of avoiding undivided highways. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to manage the routing of a set of vehicles in order to achieve the objective of avoiding left turns. In an embodiment, the vehicle routing system 1692 uses the user 1690's routing preferences and corresponding effects for at least one routing parameter to govern the routing of a set of vehicles in order to achieve the objective of avoiding a driver-operated vehicle.

[0217] Figure 17 shows a reward-based coordinated vehicle routing method 1700 according to embodiments of the systems and methods disclosed herein. In 1702, the method includes receiving user responses related to a set of vehicles for rewards provided in a reward-based interface through a reward-based interface. In 1704, the method includes determining routing preferences based on user responses. In 1706, the method includes determining at least one user action resulting from the user's response to the reward. In 1708, the method includes determining the corresponding effect of at least one user action on at least one routing parameter. In 1708, the method includes governing the routing of a set of vehicles in response to routing preferences and the corresponding effects on at least one routing parameter.

[0218] In one embodiment, user 1690 is the rider of at least one vehicle 1610 in a set of vehicles 1694. In another embodiment, user 1690 is the administrator for a set of roads used by at least one vehicle 1610 in a set of vehicles 1694. In yet another embodiment, user 1690 is the administrator for a fleet of vehicles including a set of vehicles 1694.

[0219] In an embodiment, the reward-based interface 16104 is configured for in-vehicle use. In an embodiment, at least one routing parameter 1630 includes at least one of the following: congestion, desired arrival time, preferred route, fuel efficiency, pollution reduction, accident avoidance, avoidance of bad weather, avoidance of poor road conditions, fuel consumption reduction, carbon dioxide emission reduction, local noise reduction, avoidance of high-crime areas, crowd satisfaction, speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undistributed highways, avoidance of left turns, and avoidance of operating vehicles. In an embodiment, user 1690 responds to reward 16102 by accepting the reward 16102 offered by the reward-based interface 16104, rejecting the reward 16102 offered by the reward-based interface 16104, or ignoring the reward 16102 offered by the reward-based interface 16104. In an embodiment, user 1690 indicates routing preference by either accepting or rejecting the reward 16102 offered by the reward-based interface 16104. In this embodiment, user 1690 directs routing priorities by undertaking actions in at least one vehicle 1610 within the vehicle set 1694, which facilitates the transfer of reward 16102 to user 1690.

[0220] In an embodiment, the method further comprises transmitting a signal to the vehicle routing system 1692 via the reward-providing response processing circuit 16105 to select a vehicle route that allows sufficient time for user 1690 to perform at least one user action. In an embodiment, the method further comprises transmitting a signal to the vehicle routing system 1692 via the reward-providing response processing circuit 16105, the signal indicating a vehicle destination related to at least one user action, and the vehicle routing system 1692 adjusting the route of vehicle 1695 related to at least one user action to include the destination. In an embodiment, the reward 16102 is related to achieving a vehicle routing fuel efficiency target.

[0221] In embodiments, reward 16102 is related to achieving the objective of reduced traffic in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced pollution in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced carbon footprint in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of reduced neighborhood noise in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of collective satisfaction in vehicle routing. In embodiments, reward 16102 is related to achieving the objective of accident site avoidance in vehicle routing.

[0222] In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid high-crime areas. In embodiments, the reward 16102 is related to achieving the objective of reducing traffic congestion in vehicle routing. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid bad weather. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to maximize travel time. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to maximize speed. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid toll roads. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid urban roads. In embodiments, the reward 16102 is related to achieving vehicle routing to avoid undivided highways. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing to avoid left turns. In embodiments, the reward 16102 is related to achieving the objective of vehicle routing for driven vehicles.

[0223] Referring to Figure 18, an embodiment provided herein is a transportation system 1811 having a data processing system 1862 for predicting emerging transportation needs 18112 for a group of individuals, taking data 18114 from multiple social data sources 1869 and using a neural network 18108. Of the various social data sources 18107 described above, a large amount of data is available on social groups such as groups of friends, families, colleagues at work, club members, people with common interests or affiliations, and political groups. The expert system described above can be trained to predict the transportation needs of a group, for example, using a training dataset of human predictions and / or a model with feedback of results, as described throughout. For example, based on a discussion thread of a social group, at least partially shown on a social network feed, it becomes clear that a group meeting or trip is taking place, and the system can predict when and where each participating member will need to travel to attend (using indicators such as the location of each member and a set of travel destinations). Based on such predictions, the system can automatically identify and display transportation options, such as available public transport options, flight options, and rideshare options. Such options could include things that allow the group to share transportation, such as showing a route that would involve picking up a set of group members to travel together. Social media information could include posts, tweets, comments, chats, photos, etc., and could be processed as described above.

[0224] Embodiments provided herein include a transport system 1811 comprising a data processing system 1862 for taking data 18114 from a plurality of social data sources 1869 and using a neural network 18108 to predict new transport needs 18112 for a group of individuals 18110.

[0225] Figure 19 illustrates a method 1900 for predicting common transportation needs of a group, according to embodiments of the systems and methods disclosed herein. In 1902, the method comprises collecting social media source data relating to multiple individuals, the data being sourced from multiple social media sources. In 1904, the method comprises processing the data to identify subsets of multiple individuals that form social groups based on group affiliation references in the data. In 1906, the method comprises detecting keywords in the data that indicate transportation needs. In 1908, the method comprises identifying common transportation needs for subsets of multiple individuals using a neural network trained to predict transportation needs based on detected keywords.

[0226] Referring to Figures 18 and 19, in an embodiment, the neural network 18108 is a convolutional neural network 18113. In an embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in social media to transportation activities. In an embodiment, the neural network 18108 predicts at least one destination and time of arrival for a subset 18110 of several individuals who share common transportation needs. In an embodiment, the neural network 18108 predicts common transportation needs based on an analysis of transportation need suggestive keywords detected in discussion threads among some of the individuals in a social group. In an embodiment, the method further includes identifying at least one shared transportation service 18111 that facilitates a portion of the social group meeting predicted common transportation needs 18112. In an embodiment, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up a portion of the social group.

[0227] Figure 20 illustrates a method 2000 for predicting group transportation needs according to embodiments of the systems and methods disclosed herein. In 2002, the method includes collecting social media source data relating to multiple individuals, the data being supplied from multiple social media sources. In 2004, the method includes processing the data to identify a subset of multiple individuals who share a need for group transportation. In 2006, the method includes detecting keywords in the data that indicate a need for group transportation for the subset of multiple individuals. In 2008, the method includes predicting group transportation needs using a neural network trained to predict transportation needs based on the detected keywords. In 2009, the method includes instructing a vehicle routing system to meet the need for group transportation.

[0228] Referring to Figures 18 and 20, in an embodiment, the neural network 18108 is a convolutional neural network 18113. In an embodiment, directing a vehicle routing system to meet group transport needs includes routing multiple vehicles to destinations derived from social media source data 18114. In an embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in the social media source data 18114 with transport activities. In an embodiment, the method further includes the neural network 18108 predicting at least one destination and time of arrival for a subset 18110 of multiple individuals 18109 who share group transport needs. In an embodiment, the method further includes the neural network 18108 predicting group transport needs based on an analysis of transport need suggestive keywords detected in discussion threads within the social media source data 18114. In an embodiment, the method further includes identifying at least one shared transport service 18111 that facilitates meeting the predicted group transport needs for at least a portion of the subset 18110 of multiple individuals. In one embodiment, at least one shared transport service 18111 comprises generating a vehicle route that facilitates picking up at least a portion of a subset 18110 of several individuals.

[0229] Figure 21 illustrates a method 2100 for predicting the need for mass transport according to embodiments of the systems and methods disclosed herein. In 2102, the method includes collecting social media source data from multiple social media sources. In 2104, the method includes processing the data to identify an event. In 2106, the method includes detecting keywords in the data that indicate an event to determine the need for transport associated with the event. In 2106, the method includes instructing a vehicle routing system to meet the transport needs using a neural network trained to predict transport needs based at least in part on social media source data.

[0230] Referring to Figures 18 and 21, in the embodiment, the neural network 18108 is a convolutional neural network 18113. In the embodiment, the vehicle routing system is directed to meet transportation needs by routing multiple vehicles to locations associated with an event. In the embodiment, the vehicle route control system is directed to meet transportation needs by routing multiple vehicles to avoid areas adjacent to locations associated with an event. In the embodiment, the vehicle route control system is directed to meet transportation needs by routing vehicles associated with users whose social media source data 18114 does not indicate a need for transportation to avoid areas adjacent to locations associated with an event. In the embodiment, the method further includes presenting at least one transportation service to meet transportation needs. In the embodiment, the neural network 18108 is trained on a model that facilitates matching phrases in social media sourced data 18114 with transportation activities.

[0231] In an embodiment, the neural network 18108 predicts at least one of the destination and arrival time of individuals participating in an event. In an embodiment, the neural network 18108 predicts transportation needs based on an analysis of transportation need suggestive keywords detected in discussion threads within the social media source data 18114. In an embodiment, the method further comprises identifying at least one shared transportation service that facilitates meeting the predicted transportation needs of at least a subset of individuals identified in the social media source data 18114. In an embodiment, the at least one shared transportation service includes generating a vehicle route that facilitates picking up a portion of the subset of individuals identified in the social media source data 18114.

[0232] Referring to Figure 22, an embodiment provided herein includes a traffic system 22111 having a data processing system 2211 for ingesting social media data 22114 from a plurality of social data sources 2269 of 22107, and a system 2247 for processing the social data sources 22107 using a hybrid neural network 2247 and optimizing the operating state of the traffic system 22111. The hybrid neural network 2247 includes, for example, a neural network component that performs classification or prediction based on the processing of social media data 22114 (e.g., predicting high attendance rates for an event by processing images on many social media feeds that show interest in an event from many people, predicting traffic volume, etc., classifying individual interest in a topic, and many others), and other components that optimize the operating state of the transport system, such as in-vehicle state, routing state (for individual vehicles 2210 or a collection of vehicles 2294), user experience state, or other states described through this disclosure (e.g., routing individuals early to venues such as music festivals that are likely to have very high attendance, playing music content in vehicle 2210 for bands participating in the music festival, etc.).

[0233] Embodiments provided herein include a system for transport, comprising a data processing system 2211 for taking social media data 22114 from a plurality of social data sources 2269, and using a hybrid neural network 2247 to optimize the operating state of the transport system based on processing the data 22114 from the plurality of social data sources 2269 with the hybrid neural network 2247.

[0234] Embodiments provided herein include a hybrid neural network system 22115 for optimizing a transport system, the hybrid neural network system 22115 comprising a hybrid network 2247, which includes a first neural network 2222 that predicts local effects 22116 on the transport system through analysis of social media data 22114 supplied from a plurality of 2269 social media data sources 22107, and a second neural network 2220 that optimizes the operating state of the transport system based on the predicted local effects 22116.

[0235] In an embodiment, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In an embodiment, the second neural network 2220 is to optimize the in-vehicle lidar experience state. In an embodiment, the first neural network 2222 identifies a set of vehicles 2294 that contribute to a local effect 22116 based on the correlation between vehicle position and the region of the local effect 22116. In an embodiment, the second neural network 2220 is to optimize the routing state of the transport system for vehicles adjacent to the location of the local effect 22116. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on keywords in social media data that indicate the results of transport system optimization actions. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on social media posts.

[0236] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media feeds. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on evaluations derived from social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on like / dislike activity detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on relational metrics in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on user behavior detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on discussion threads in social media data 22114.

[0237] In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on chats in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photos in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic impact information in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on the appearance of a particular individual at a location in the social media data 22114. In an embodiment, the particular individual is a celebrity. In an embodiment, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on the presence of rare or transient phenomena at a location in the social media data 22114.

[0238] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize commerce-related events at locations within the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize entertainment events at locations within the social media data 22114. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes traffic conditions. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes weather conditions. In an embodiment, the social media data analyzed to predict local impacts on a traffic system includes entertainment options.

[0239] In embodiments, social media data analyzed to predict local impacts on a transportation system includes risk-related conditions. In embodiments, risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, social media data analyzed to predict local impacts on a transportation system includes commercial-related conditions. In embodiments, social media data analyzed to predict local impacts on a transportation system includes objective-related conditions.

[0240] In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes estimates of event attendees. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes predictions of event attendees. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes modes of transport. In an embodiment, modes of transport include automobile traffic. In an embodiment, modes of transport include public transport options.

[0241] In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes hashtags. In an embodiment, the social media data analyzed to predict local impacts on the transportation system includes topic trending. In an embodiment, the outcome of the transportation system optimization action is a reduction in fuel consumption. In an embodiment, the outcome of the transportation system optimization action is a reduction in traffic congestion. In an embodiment, the outcome of the transportation system optimization action is a reduction in pollution. In an embodiment, the outcome of the transportation system optimization action is the avoidance of adverse weather conditions. In an embodiment, the operating state of the transportation system to be optimized includes the in-vehicle state. In an embodiment, the operating state of the transportation system to be optimized includes the routing state.

[0242] In one embodiment, the routing state is for an individual vehicle 2210. In another embodiment, the routing state is for a set of vehicles 2294. In yet another embodiment, the operating state of the optimized transport system includes the user experience state.

[0243] Figure 23 shows a method 2300 for optimizing the operational state of a transport system according to embodiments of the systems and methods disclosed herein. In 2302, the method includes collecting social media source data relating to multiple individuals, the data being supplied from multiple social media sources. In 2306, the method includes predicting the impact on the transport system through analysis of data supplied from social media by a first neural network of the hybrid neural network. In 2308, the method includes optimizing at least one operational state of the transport system in response to the predicted impact by a second neural network of the hybrid neural network.

[0244] Referring to Figures 22 and 23, in the embodiment, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In the embodiment, the second neural network 2220 optimizes the in-vehicle LiDAR experience state. In the embodiment, the first neural network 2222 identifies a set of vehicles contributing to the effect based on the correlation between vehicle position and the effect area. In the embodiment, the second neural network 2220 optimizes the routing state of the traffic system for vehicles close to the location of the effect.

[0245] In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on keywords in social media data that indicate the results of traffic system optimization actions. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media posts. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on social media feeds. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on evaluations derived from social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on like / dislike activity detected in social media data 22114. In an embodiment, the hybrid neural network 2247 is trained to predict and optimize based on relational indicators in social media data 22114.

[0246] In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on user actions detected in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on discussion threads in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on chats in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on photos in the social media data 22114. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on information that affects traffic in the social media data 22114.

[0247] In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on the display of a particular individual at a location within the social media data. In an embodiment, the particular individual is a celebrity. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization based on the presence of a rare or transient phenomenon at a location within the social media data. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization to predict commerce-related events at a location within the social media data. In an embodiment, the hybrid neural network 2247 is trained for at least one of prediction and optimization to predict entertainment events at a location within the social media data. In an embodiment, the social media data analyzed to predict the impact on the transportation system includes traffic conditions.

[0248] In an embodiment, the social media data analyzed to predict the impact on a transportation system includes weather conditions. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes entertainment options. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes risk-related conditions. In an embodiment, the risk-related conditions include crowds gathering for potentially dangerous reasons. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes business-related conditions. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes goal-related conditions.

[0249] In an embodiment, the social media data analyzed to predict the impact on a transportation system includes an estimated value of event attendees. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes a prediction of event attendees. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes transportation modes. In an embodiment, the transportation modes include automobile traffic. In an embodiment, the transportation modes include options for public transportation. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes hashtags. In an embodiment, the social media data analyzed to predict the impact on a transportation system includes topic trending.

[0250] In an embodiment, the outcome of the traffic system optimization action is to reduce fuel consumption. In an embodiment, the outcome of the traffic system optimization action is to reduce traffic congestion. In an embodiment, the outcome of the traffic system optimization action is to reduce pollution. In an embodiment, the outcome of the traffic system optimization action is to avoid bad weather. In an embodiment, the operating state of the traffic system to be optimized includes the in-vehicle state. In an embodiment, the operating state of the transportation system to be optimized includes the routing state. In an embodiment, the routing state is for individual vehicles. In an embodiment, the routing state is for a set of vehicles. In an embodiment, the operating state of the transportation system to be optimized includes the user experience state.

[0251] Figure 24 shows a method 2400 for optimizing the operating state of a transport system according to embodiments of the systems and methods disclosed herein. In 2402, the method includes classifying social media data supplied from multiple social media sources as affecting the transport system using a first neural network of a hybrid neural network. In 2404, the method includes predicting at least one operating objective of the transport system based on the classified social media data using a second network of a hybrid neural network. In 2406, the method includes optimizing the operating state of the transport system to achieve at least one operating objective of the transport system using a third network of a hybrid neural network.

[0252] Referring to Figures 22 and 24, in this embodiment, at least one of the neural networks in the hybrid neural network 2247 is a convolutional neural network.

[0253] Referring to Figure 25, embodiments provided herein include a transport system 2511 having a data processing system 2562 for taking social media data 25114 from multiple social data sources 25107, and a hybrid neural network 2547 can be used to optimize the operating state 2545 of a vehicle 2510 based on processing the social data sources using the hybrid neural network 2547. In embodiments, the hybrid neural network 2547 may include one neural network category for prediction, another for classification, and another for optimizing one or more operating states, such as based on the optimization of one or more desired outcomes (such as providing efficient travel, a satisfying rider experience, a comfortable ride, or on-time arrival). Social data sources 2569 may be used by different neural network categories (such as any of the types described herein) for purposes such as predicting travel time, classifying content such as profiling user interests, or predicting the purpose of a transport plan (such as providing overall satisfaction to an individual or group). Social data sources 2569 can also inform optimization by providing indicators of successful outcomes (for example, a social data source 25107 like a Facebook feed might indicate that a trip was "great" or "terrible," a Yelp review might indicate that a restaurant was terrible, etc.). Thus, social data sources 2569 can be used to train the system to optimize travel planning by contributing to outcome tracking, which relates to timing, destination, purpose of travel, which individuals to invite, which entertainment options to choose, and many other things.

[0254] Embodiments provided herein include a transport system 2511 comprising a data processing system 2562 for taking in social media data 25114 from a plurality of social data sources 25107, and a hybrid neural network 2546 for optimizing the operating state 2545 of a vehicle 2510 based on processing the data 25114 from the plurality of social data sources 25107 with a hybrid neural network 2547.

[0255] Figure 26 shows a method 2600 for optimizing the operating state of a vehicle according to embodiments of the systems and methods disclosed herein. In 2602, the method includes classifying social media data 25119 (Figure 25) supplied from multiple social media sources as affecting a transport system, using a first neural network 2522 (Figure 25) of a hybrid neural network. In 2604, the method includes predicting one or more effects 25118 (Figure 25) of the classified social media data on the transport system, using a second neural network 2520 (Figure 25) of a hybrid neural network. In 2606, the method includes optimizing the state of at least one vehicle in the transport system using a third neural network 25117 (Figure 25) of a hybrid neural network, the optimization including addressing the effects of one or more predicted effects on at least one vehicle.

[0256] Referring to Figures 25 and 26, in an embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In an embodiment, social media data 25114 includes social media posts. In an embodiment, social media data 25114 includes social media feeds. In an embodiment, social media data 25114 includes like / dislike activity detected on social media. In an embodiment, social media data 25114 includes relational metrics. In an embodiment, social media data 25114 includes user behavior. In an embodiment, social media data 25114 includes discussion threads. In an embodiment, social media data 25114 includes chats. In an embodiment, social media data 25114 includes photos.

[0257] In embodiments, social media data 25114 includes traffic affection information. In embodiments, social media data 25114 includes the appearance of a specific individual at a location. In embodiments, social media data 25114 includes the appearance of a celebrity at a location. In embodiments, social media data 25114 includes the presence of rare or transient phenomena at a location. In embodiments, social media data 25114 includes commerce-related events. In embodiments, social media data 25114 includes entertainment events at a location. In embodiments, social media data 25114 includes traffic conditions. In embodiments, social media data 25114 includes weather conditions. In embodiments, social media data 25114 includes entertainment options.

[0258] In embodiments, social media data 25114 includes risk-related conditions. In embodiments, social media data 25114 includes predictions of event attendance. In embodiments, social media data 25114 includes estimates of event attendance. In embodiments, social media data 25114 includes means of transportation used in conjunction with the event. In embodiments, the effect on transportation 25118 includes a reduction in fuel consumption. In embodiments, the effect on transportation systems 25118 includes a reduction in traffic congestion. In embodiments, the effect on transportation systems 25118 includes a reduction in carbon footprint. In embodiments, the effect on transport systems 25118 includes reduced pollution.

[0259] In an embodiment, the optimized state 2544 of at least one vehicle 2510 is the vehicle's operating state 2545. In an embodiment, the optimized state of at least one vehicle includes an in-vehicle state. In an embodiment, the optimized state of at least one vehicle includes a lidar state. In an embodiment, the optimized state of at least one vehicle includes a routing state. In an embodiment, the optimized state of at least one vehicle includes a user experience state. In an embodiment, characterization of the optimization results in social media data 25114 is used as feedback to improve the optimization. In an embodiment, the feedback includes likes and dislikes of the results. In an embodiment, the feedback includes social media activities that refer to the results.

[0260] In embodiments, feedback includes trending social media activities that reference the results. In embodiments, feedback includes hashtags related to the results. In embodiments, feedback includes evaluation of the results. In embodiments, feedback includes requests for the results.

[0261] Figure 26A shows a method 26A00 for optimizing the operating state of a vehicle according to embodiments of the systems and methods disclosed herein. In 26A02, the method includes classifying social media data supplied from multiple social media sources as affecting a transport system, using a first neural network of a hybrid neural network. In 26A04, the method includes predicting at least one vehicle operating objective for the transport system based on the classified social media data, using a second neural network of a hybrid neural network. In 26A06, the method includes optimizing the state of a vehicle in a transport system to achieve at least one vehicle operating objective for the transport system, using a third neural network of a hybrid neural network.

[0262] Referring to Figures 25 and 26A, in this embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In this embodiment, the vehicle operation objective is to achieve the rider state of at least one rider in the vehicle. In this embodiment, the social media data 25114 includes social media posts.

[0263] In an embodiment, social media data 25114 includes social media feeds. In an embodiment, social media data 25114 includes like / dislike activity detected on social media. In an embodiment, social media data 25114 includes relational indicators. In an embodiment, social media data 25114 includes user behavior. In an embodiment, social media data 25114 includes discussion threads. In an embodiment, social media data 25114 includes chats. In an embodiment, social media data 25114 includes photos. In an embodiment, social media data 25114 includes traffic affection information.

[0264] In embodiments, social media data 25114 includes the presence of a specific individual at a location. In embodiments, social media data 25114 includes the presence of a celebrity at a location. In embodiments, social media data 25114 includes the presence of a rare or transient phenomenon at a location. In embodiments, social media data 25114 includes commerce-related events. In embodiments, social media data 25114 includes entertainment events at a location. In embodiments, social media data 25114 includes traffic conditions. In embodiments, social media data 25114 includes weather conditions. In embodiments, social media data 25114 includes entertainment options.

[0265] In embodiments, social media data 25114 includes risk-related conditions. In embodiments, social media data 25114 includes predictions of event attendance. In embodiments, social media data 25114 includes estimations of event attendance. In embodiments, social media data 25114 includes modes of transportation used with the event. In embodiments, effects on transportation include reduced fuel consumption. In embodiments, effects on the transportation system include reduced traffic congestion. In embodiments, effects on the transportation system include a reduction in carbon footprint. In embodiments, effects on the transport system include reduced pollution. In embodiments, the optimized state of the vehicle is the operating state of the vehicle.

[0266] In an embodiment, the optimized state of the vehicle includes the in-vehicle state. In an embodiment, the optimized state of the vehicle includes the lidar state. In an embodiment, the optimized state of the vehicle includes the routing state. In an embodiment, the optimized state of the vehicle includes the user experience state. In an embodiment, characterization of the optimization results in social media data is used as feedback to improve the optimization. In an embodiment, the feedback includes likes and dislikes of the results. In an embodiment, the feedback includes social media activities referencing the results. In an embodiment, the feedback includes trending social media activities referencing the results.

[0267] In an embodiment, the feedback includes hashtags associated with the results. In an embodiment, the feedback includes evaluations of the results. In an embodiment, the feedback includes requests for the results.

[0268] Referring to Figure 27, embodiments provided herein include a transport system 2711 having a data processing system 2762 for taking social data 27114 from a plurality of social data sources 2769, and using a hybrid neural network 2746 to optimize the satisfaction 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data sources using a hybrid neural network 2747. The social data sources 2769 may be used, for example, by one neural network category to predict which entertainment options are most effective for the rider 27120, and another neural network category may be used to optimize route planning (for example, based on social data indicating likely traffic, points of interest, etc.). The social data 27114 may also be used for result tracking and feedback to optimize the system with respect to entertainment options, traffic planning, routing, etc.

[0269] Aspects provided herein include a transportation system 2711 comprising a data processing system 2762 for capturing social data 27114 from a plurality of social data sources 2769, and a hybrid neural network 2746 for optimizing the satisfaction level 27121 of at least one rider 27120 in a vehicle 2710 based on processing social data 27114 from the plurality of social data sources 2769 using a hybrid neural network 2747.

[0270] FIG. 28 is a diagram illustrating a method 2800 for optimizing rider satisfaction in accordance with an embodiment of the systems and methods disclosed herein. At 2802, the method includes classifying social media data 27119 (FIG. 27) supplied from a plurality of social media sources as indicative of an impact on the transportation system using a first neural network 2722 (FIG. 27) of a hybrid neural network. At 2804, the method includes predicting at least one aspect 27122 (FIG. 27) of rider satisfaction that is affected by an impact on the transportation system obtained from the social media data classified as indicative of an impact on the transportation system using a second neural network 2720 (FIG. 27) of the hybrid neural network. At 2806, the method includes optimizing at least one aspect of rider satisfaction for at least one rider occupying a vehicle within the transportation system using a third neural network 27117 (FIG. 27) of the hybrid neural network.

[0271] Referring to Figures 27 and 28, in an embodiment, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In an embodiment, at least one aspect 27121 of rider satisfaction is optimized by predicting entertainment options to present to the rider. In an embodiment, at least one aspect 27121 of rider satisfaction is optimized by optimizing the route plan for the vehicle occupied by the rider. In an embodiment, at least one aspect 27121 of rider satisfaction is the rider state, and optimization of the aspect of rider satisfaction is performed, which includes optimizing the rider state. In an embodiment, social media data specific to the rider is analyzed to determine at least one optimization action that is likely to optimize at least one aspect 27121 of rider satisfaction. In an embodiment, the optimization action is selected from a group of actions consisting of adjusting the route plan to include passing through points of interest to the user, avoiding traffic congestion predicted from social media data, and presenting entertainment options.

[0272] In an embodiment, social media data includes social media posts. In an embodiment, social media data includes social media feeds. In an embodiment, social media data includes like / dislike activity detected on social media. In an embodiment, social media data includes relationship indicators. In an embodiment, social media data includes user behavior. In an embodiment, social media data includes discussion threads. In an embodiment, social media data includes chats. In an embodiment, social media data includes photos.

[0273] In embodiments, social media data includes information that affects traffic. In embodiments, social media data includes the presence of a specific individual in a particular location. In embodiments, social media data includes the presence of a celebrity in a particular location. In embodiments, social media data includes the presence of a rare or transient phenomenon in a particular location. In embodiments, social media data includes commerce-related events. In embodiments, social media data includes entertainment events in a particular location. In embodiments, social media data includes traffic conditions. In embodiments, social media data includes weather conditions. In embodiments, social media data includes entertainment options. In embodiments, social media data includes risk-related conditions. In embodiments, social media data includes predictions of event attendance. In embodiments, social media data includes estimates of event attendance. In embodiments, social media data includes modes of transportation used in conjunction with the event. In embodiments, the effect on transportation includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reducing the carbon footprint. In embodiments, the effect on the transport system includes reducing pollution. In embodiments, at least one optimized aspect of rider satisfaction is the operating state of the vehicle. In an embodiment, at least one optimized aspect of rider satisfaction includes the in-vehicle state. In an embodiment, at least one optimized aspect of rider satisfaction includes the rider state. In an embodiment, at least one optimized aspect of rider satisfaction includes the routing state. In an embodiment, at least one optimized aspect of rider satisfaction includes the user experience state.

[0274] In embodiments, characterization of optimization results in social media data is used as feedback to improve the optimization. In embodiments, the feedback includes likes and dislikes of the results. In embodiments, the feedback includes social media activities referencing the results. In embodiments, the feedback includes trending social media activities referencing the results. In embodiments, the feedback includes hashtags related to the results. In embodiments, the feedback includes evaluations of the results. In embodiments, the feedback includes requests for the results.

[0275] Embodiments provided herein include a rider satisfaction system 27123 for optimizing rider satisfaction 27121, the system comprising: a first neural network 2722 of a hybrid neural network 2747 for classifying social media data 27114 supplied from a plurality of social media sources 27107 as indicating an impact on a traffic system 2711; a second neural network 2720 of the hybrid neural network 2747 for predicting at least one aspect 27122 of rider satisfaction 27121 that is affected by the impact on the traffic system, obtained from the social media data classified as indicating an impact on the traffic system; and a third neural network 27117 of the hybrid neural network 2747 for optimizing at least one aspect 27121 of rider satisfaction for at least one rider 2744 occupying a vehicle 2710 in the traffic system 2711. In embodiments, at least one of the neural networks of the hybrid neural network 2747 is a convolutional neural network.

[0276] In an embodiment, at least one aspect of rider satisfaction 27121 is optimized by predicting entertainment options to present to rider 2744. In an embodiment, at least one aspect of rider satisfaction 27121 is optimized by optimizing route planning for vehicles 2710 occupied by rider 2744. In an embodiment, at least one aspect of rider satisfaction 27121 is rider state 2737, and optimizing at least one aspect of rider satisfaction 27121 consists of optimizing rider state 2737. In an embodiment, social media data specific to rider 2744 is analyzed to determine at least one optimization action that is likely to optimize at least one aspect of rider satisfaction 27121. In an embodiment, at least one optimization action is selected from the group consisting of adjusting route planning to include waypoints of interest to the user, avoiding traffic congestion predicted from social media data, obtaining economic benefits, obtaining altruistic benefits, and presenting entertainment options.

[0277] In an embodiment, the economic benefit is fuel saved. In an embodiment, the altruistic benefit is a reduction in environmental impact. In an embodiment, social media data includes social media posts. In an embodiment, social media data includes social media feeds. In an embodiment, social media data includes like / dislike activity detected on social media. In an embodiment, social media data includes relational indicators. In an embodiment, social media data includes user behavior. In an embodiment, social media data includes discussion threads. In an embodiment, social media data includes chats. In an embodiment, social media data includes photos. In an embodiment, social media data includes information that influences traffic. In an embodiment, social media data includes the display of a specific individual in a particular location.

[0278] In an embodiment, social media data includes the appearance of celebrities at the location. In an embodiment, social media data includes the presence of rare or transient phenomena at the location. In an embodiment, social media data includes commercial events. In an embodiment, social media data includes entertainment events at the location. In an embodiment, social media data includes traffic conditions. In an embodiment, social media data includes weather conditions. In an embodiment, social media data includes entertainment options. In an embodiment, social media data includes risk-related conditions. In an embodiment, social media data includes predictions of event attendance. In an embodiment, social media data includes estimates of event attendance. In an embodiment, social media data includes means of transportation used in conjunction with the event.

[0279] In an embodiment, the effect on the traffic system includes reducing fuel consumption. In an embodiment, the effect on the traffic system includes reducing traffic congestion. In an embodiment, the effect on the transport system includes reducing the carbon footprint. In an embodiment, the effect on the transport system includes reducing pollution. In an embodiment, at least one optimized aspect of rider satisfaction is the operating state of the vehicle. In an embodiment, at least one optimized aspect of rider satisfaction includes the in-vehicle state. In an embodiment, at least one optimized aspect of rider satisfaction includes the rider state. In an embodiment, at least one optimized aspect of rider satisfaction includes the routing state. In an embodiment, at least one optimized aspect of rider satisfaction includes the user experience state. In an embodiment, characterization of the optimization results in social media data is used as feedback to improve the optimization. In an embodiment, the feedback includes likes and dislikes of the results. In an embodiment, the feedback includes social media activities referencing the results. In an embodiment, the feedback includes trending social media activities referencing the results. In an embodiment, the feedback includes hashtags related to the results. In an embodiment, the feedback includes evaluations of the results. In an embodiment, the feedback includes requests for the results.

[0280] Referring to Figure 29, an embodiment provided herein is a transport system 2911 having a hybrid neural network 2947, in which one neural network 2922 processes sensor inputs 29125 relating to the lidar 2944 of a vehicle 2910 to determine an emotional state 29126, and another neural network optimizes at least one operating parameter 29124 of the vehicle to improve the emotional state 2966. For example, a neural network 2922, including one or more perceptrons 29127 that mimic human sensations, is used to mimic or assist in determining the likely emotional states of the lidar 29126 based on the degree to which various sensations have been stimulated, and another neural network 2920 is used for an expert system that performs random and / or systematized fluctuations of various combinations of operating parameters (such as entertainment settings, seat settings, suspension settings, and route types), optionally using genetic programming to promote preferred combinations and eliminate unfavorable combinations based on inputs from the output of the perceptron-containing neural network 2922 that predicts emotional states. These and many other such combinations are covered by this disclosure. In Figure 29, the perceptron 29127 is depicted as optional.

[0281] Embodiments provided herein are a transport system 2911 characterized in that one neural network 2922 processes sensor inputs 29125 corresponding to a lidar 2944 of a vehicle 2910 to determine the emotional state 2966 of the lidar 2944, and another neural network 2920 comprises a hybrid neural network 2947 that optimizes at least one operating parameter 29124 of the vehicle to improve the emotional state 2966 of the lidar 2944.

[0282] Embodiments provided herein are a hybrid neural network 2947 for rider satisfaction, comprising a rider emotional state detection device 2917, which includes a first neural network 2922 for detecting a detected emotional state 29126 of a rider 2944 occupying a vehicle 2910 through analysis of sensor inputs 29125 collected from sensors 2925 deployed in a vehicle 2910 to collect the physiological state of the rider, and a second neural network 2920 for optimizing vehicle operating parameters 29124 according to the rider's preferred emotional state 29126.

[0283] In an embodiment, the first neural network 2922 is a recurrent neural network, and the second neural network 2920 is a radial basis function neural network. In an embodiment, at least one of the neural networks in the hybrid neural network 2947 is a convolutional neural network. In an embodiment, the second neural network 2920 optimizes the operation parameters 29124 based on the correlation between the vehicle operating state 2945 and the ridor's emotional state 2966. In an embodiment, the second neural network 2920 optimizes the operation parameters 29124 in real time in response to the detection of the detected emotional state 29126 of the ridor 2944 by the first neural network 2922. In an embodiment, the first neural network 2922 consists of a plurality of connected nodes that form a directed cycle, and the first neural network 2922 further facilitates the bidirectional flow of data between the connected nodes. In the embodiment, the optimized operational parameter 29124 affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, the proximity to objects along the path, and the proximity to other vehicles along the path.

[0284] Embodiments provided herein are artificial intelligence systems 2936 for optimizing rider satisfaction, which include a hybrid neural network 2947 (which may be a recurrent neural network in the figure, for example; in Figure 29, the neural network 2922 may be a recurrent neural network) and a radial basis function neural network (which may be a radial basis function neural network in Figure 2920) for indicating changes in the emotional state of a rider 2944 riding in a vehicle 2910 through recognition of patterns in the rider's physiological data captured by at least one sensor 2925 deployed to capture data indicating the rider's emotional state while riding in the vehicle 2910, to optimize vehicle driving parameters 29124 in response to instructions for changes in the rider's emotional state in order to achieve a preferred emotional state for the rider. In embodiments, the vehicle driving parameters 29124 to be optimized are determined and adjusted to induce a preferred emotional state for the rider.

[0285] Embodiments provided herein are artificial intelligence systems 2936 for optimizing rider satisfaction, comprising: a hybrid neural network 2947 including: a convolutional neural network for indicating changes in the emotional state of a rider in a vehicle through recognition of patterns in the rider's visual data captured by at least one image sensor (in Figure 29, neural network 1, depicted by reference numeral 2922, may optionally be a convolutional neural network); a second neural network 2920 for indicating changes in the emotional state of a rider in a vehicle through recognition of patterns in the rider's visual data captured by 29 (in Figure 29, network 2, depicted by reference numeral 2922, may optionally be a neural network); and a second neural network 2920 for optimizing vehicle driving parameters 29124 in response to instructions for changes in the rider's emotional state in order to achieve a preferred emotional state for the rider.

[0286] In one embodiment, the vehicle operation parameters 19124 to be optimized are determined and adjusted to induce a desirable emotional state in the rider.

[0287] Referring to Figure 30, embodiments provided herein offer a transport system 3011 having an artificial intelligence system 3036 for processing feature vectors of a rider's face image in the vehicle to determine an emotional state and for optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. Faces can be classified based on images from an in-vehicle camera, a camera of an available mobile phone or other mobile device, or from other sources. An expert system, optionally trained on a training set of data provided by a human or trained by deep learning, may learn to adjust vehicle parameters (such as any described herein) to provide an improved emotional state. For example, if the rider's face indicates stress, the vehicle may select a less stressful route, play relaxing music, play humorous content, etc.

[0288] Embodiments provided herein are a transport system 3011 comprising: an artificial intelligence system 3036 for processing feature vectors of an image 30129 of a face 30128 of a lidar 3044 in a vehicle 3010 to determine the lidar's emotional state 3066; and an artificial intelligence system for optimizing vehicle operation parameters 30124 to improve the emotional state 3066 of the lidar 3044.

[0289] In this embodiment, the artificial intelligence system 3036 includes: a first neural network 3022 that detects the rider's emotional state 30126 through the recognition of a pattern of feature vectors 30130 of an image 30129 of the rider's face 30128 in a vehicle 3010, wherein the feature vectors 30130 indicate at least one of the rider's favorable emotional state and the rider's unfavorable emotional state; and a second neural network 3020 that optimizes the vehicle's operating parameters 30124 in response to the detected rider's emotional state 30126 in order to achieve the rider's favorable emotional state.

[0290] In an embodiment, the first neural network 3022 is a recurrent neural network, and the second neural network 3020 is a radial basis function neural network. In an embodiment, the second neural network 3020 optimizes the operation parameter 30124 based on the correlation between the vehicle operating state 3045 and the rider's emotional state 3066. In an embodiment, the second neural network 3020 determines the optimal value of the vehicle's operation parameter, and the transport system 3011 adjusts the vehicle's operation parameter 30124 to the optimal value to induce a favorable emotional state for the rider. In an embodiment, the first neural network 3022 further learns to classify feature vector patterns by processing the training dataset 30131 and to associate the patterns with a set of emotional states and their changes. In one embodiment, the training dataset 30131 is supplied from at least one of the following data streams: an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, rider headgear, and a rider voice recognition system.

[0291] In an embodiment, the second neural network 3020 optimizes the operation parameters 30124 in real time in response to the detection of the lidar's emotional state by the first neural network 3022. In an embodiment, the first neural network 3022 detects patterns in feature vectors. In an embodiment, the patterns are associated with a change in the lidar's emotional state from a first emotional state to a second emotional state. In an embodiment, the second neural network 3020 optimizes the vehicle's driving parameters in response to the detection of patterns associated with the change in emotional state. In an embodiment, the first neural network 3022 comprises a plurality of interconnected nodes that form a directed cycle, and the first neural network 3022 further facilitates the bidirectional flow of data between the interconnected nodes. In one embodiment, the transport system 3011 is a feature vector generation system for processing a set of images of a lidar's face, wherein the processing of the set of images is to generate a feature vector 30130 of the lidar's face images, where the set of images is captured over time from a plurality of image capture devices 3027 while the lidar 3044 is in the vehicle 3010. In another embodiment, the transport system further comprises an image capture device 3027 arranged to capture a set of images of a lidar's face inside the vehicle from a plurality of viewpoints, and an image processing system that generates a feature vector from a set of images captured from at least one of the plurality of viewpoints.

[0292] In an embodiment, the transport system 3011 further comprises an interface 30133 between a first neural network and an image processing system 30132 for communicating a time sequence of feature vectors, wherein the feature vectors represent the emotional state of the lidar. In an embodiment, the feature vectors represent at least one of the following: a change in the lidar's emotional state, a stable emotional state of the lidar, a rate of change in the lidar's emotional state, a direction of change in the lidar's emotional state, a polarity of change in the lidar's emotional state, a change in the lidar's emotional state to an undesirable emotional state, and a change in the lidar's emotional state to a desirable emotional state.

[0293] In embodiments, the operational parameters to be optimized affect at least one of the following: the vehicle's path, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In embodiments, a second neural network interacts with the vehicle control system to adjust the operational parameters. In embodiments, the artificial intelligence system further comprises a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In embodiments, the artificial intelligence system includes a recurrent neural network that directs changes in the rider's emotional state through the recognition of a pattern of feature vectors of an image of the rider's face in the vehicle, and a radial basis function neural network that optimizes the vehicle's operational parameters to achieve a preferred emotional state for the rider in response to the directives for changes in the rider's emotional state.

[0294] In an embodiment, the radial basis function neural network optimizes operational parameters based on the correlation between the vehicle's operating state and the rider's emotional state. In an embodiment, the vehicle's driving parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the recurrent neural network further learns to associate the feature vector patterns with emotional states and their changes by classifying feature vector patterns from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems. In an embodiment, the radial basis function neural network optimizes operational parameters in real time in response to the recurrent neural network's detection of changes in the rider's emotional state. In an embodiment, the recurrent neural network detects feature vector patterns indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the radial basis function neural network optimizes the vehicle's operating parameters in response to the indicated change in emotional state.

[0295] In an embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the feature vector indicates that the lidar's emotional state is changing, the lidar's emotional state is stable, the rate of change in the lidar's emotional state, the direction of change in the lidar's emotional state, and the polarity of change in the lidar's emotional state, indicating that the lidar's emotional state is changing to an undesirable state and the lidar's emotional state is changing to a favorable state. In an embodiment, the operational parameters to be optimized affect at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, proximity to objects along the path, and proximity to other vehicles along the path.

[0296] In an embodiment, the radial basis function neural network interacts with the vehicle control system 30134 to adjust the operational parameters 30124. In an embodiment, the artificial intelligence system 3036 further comprises a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the Rider's emotional state based on the degree to which at least one of the Rider's senses is stimulated. In an embodiment, the artificial intelligence system 3036 maintains the Rider's preferred emotional state via a modular neural network, which comprises a Rider Emotional State Determination Neural Network that processes feature vectors of an image of the Rider's face in the vehicle to detect patterns. In an embodiment, the pattern of the feature vectors indicates at least one of a preferred emotional state and an unpredictable emotional state, which comprises an intermediary circuit that converts data from the Rider Emotional State Determination Neural Network into vehicle operational state data, and a vehicle operational state optimization neural network that adjusts the vehicle's operational parameters in response to the vehicle operational state data.

[0297] In an embodiment, the vehicle operating state optimization neural network adjusts vehicle operating parameters 30124 to achieve a preferred emotional state for the rider. In an embodiment, the vehicle operating state optimization neural network optimizes operating parameters based on the correlation between vehicle operating states 3045 and rider emotional states 3066. In an embodiment, the vehicle driving parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the rider emotional state determination neural network classifies feature vector patterns from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems, and further learns to associate feature vector patterns with emotional states and their changes.

[0298] In one embodiment, the vehicle driving state optimization neural network optimizes driving parameters 30124 in real time in response to the detection of changes in the rider's emotional state 30126 by the rider emotional state determination neural network. In another embodiment, the rider emotional state determination neural network detects patterns of feature vectors 30130 indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In yet another embodiment, the vehicle driving state optimization neural network optimizes the vehicle's driving parameters in response to the indicated change in emotional state. In yet another embodiment, the artificial intelligence system 3036 comprises multiple connected nodes that form a directed cycle, and the artificial intelligence system further facilitates the bidirectional flow of data between the connected nodes.

[0299] In an embodiment, the feature vector 30130 indicates at least one of the following: the Rider's emotional state is changing, the Rider's emotional state is stable, the rate of change in the Rider's emotional state, the direction of change in the Rider's emotional state, and the polarity of change in the Rider's emotional state, indicating that the Rider's emotional state is changing to an undesirable state and that the Rider's emotional state is changing to a favorable state. In an embodiment, the driving parameters to be optimized affect at least one of the following: the vehicle route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In an embodiment, the vehicle driving state optimization neural network interacts with the vehicle control system to adjust the driving parameters.

[0300] In an embodiment, the artificial intelligence system 3036 further includes a neural network comprising one or more perceptrons that mimic human sensations, which facilitates determining the emotional state of the rider based on the degree to which at least one of the rider's senses is stimulated. The terms “neural network” and “neural network” will be understood to be used interchangeably in this disclosure. In an embodiment, the rider emotional state determination neural network comprises one or more perceptrons that mimic human sensations, which facilitates determining the emotional state of the rider based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, the artificial intelligence system 3036 includes a recurrent neural network that indicates changes in the emotional state of the rider in the vehicle through the recognition of a pattern of feature vectors of an image of the rider's face in the vehicle, and the transport system further comprises a vehicle control system 30134 that controls the operation of the vehicle by adjusting a plurality of vehicle operation parameters 30124, and a feedback loop that transmits indicated changes in the rider's emotional state between the vehicle control system 30134 and the artificial intelligence system 3036. In one embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters 30124 in response to an indicated change in the rider's emotional state. In another embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters based on a correlation between the vehicle operation state and the rider's emotional state.

[0301] In an embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters 30124 that indicate a preferred rider emotional state. In an embodiment, the vehicle control system 30134 selects at least one of a plurality of vehicle operation parameters 30124 that indicates a preferred rider emotional state. In an embodiment, the recurrent neural network further learns to classify feature vector patterns from a training dataset 30131 supplied from at least one of the data flows from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice system, and associate them with emotional states and their changes. In an embodiment, the vehicle control system 30134 adjusts at least one of the plurality of vehicle operation parameters 30124 in real time. In an embodiment, the recurrent neural network detects a feature vector pattern indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the vehicle driving control system adjusts the vehicle driving parameters in response to the indicated change in emotional state. In one embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes.

[0302] In embodiments, the feature vector indicates that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state or changing to a favorable state. In embodiments, at least one of a plurality of responsively tuned vehicle driving parameters affects the vehicle's route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, at least one of a plurality of responsively tuned vehicle operation parameters affects the operation of the vehicle's powertrain and suspension system. In embodiments, the radial basis function neural network interacts with the recurrent neural network via an intermediary component of an artificial intelligence system 3036 that generates vehicle control data showing the rider's emotional state response to the vehicle's current operating state. In one embodiment, the recognition of a feature vector pattern includes processing the feature vectors of the LiDAR face image captured before adjusting at least one of the multiple vehicle operation parameters, during adjusting at least one of the multiple vehicle operation parameters, and between at least two of the following points: after adjusting at least one of the multiple vehicle operation parameters.

[0303] In an embodiment, adjusting at least one of a plurality of vehicle operation parameters 30124 improves the emotional state of the rider in the vehicle. In an embodiment, adjusting at least one of a plurality of vehicle operation parameters changes the rider's emotional state from an undesirable emotional state to a favorable emotional state. In an embodiment, the change is indicated by a recurrent neural network. In an embodiment, the recurrent neural network directs a change in the rider's emotional state in response to a change in the vehicle's driving parameters by determining the difference between a first set of feature vectors of the rider's face image captured before adjusting at least one of the plurality of driving parameters and a second set of feature vectors of the rider's face image captured during or after adjusting at least one of the plurality of driving parameters.

[0304] In one embodiment, a recurrent neural network detects a pattern of feature vectors indicating that the lidar's emotional state is changing from a first emotional state to a second emotional state. In another embodiment, a vehicle driving control system adjusts the vehicle's driving parameters in response to the indicated change in emotional state.

[0305] Referring to Figure 31, in an embodiment, what is provided herein is a transportation system having an artificial intelligence system for processing the voice of a rider in a vehicle to determine their emotional state and for optimizing at least one operating parameter of the vehicle to improve the rider's emotional state. The voice analysis module takes voice input and, using a training set of labeled data indicating the emotional state of an individual while they are speaking and / or whether others tag the data to indicate the emotional state perceived while the individual is speaking, a machine learning system (such as one of the types described herein) may be trained (using supervised learning, deep learning, etc.) to classify an individual's emotional state based on the voice. The machine learning system may improve the classification by using feedback from a large set of trials, the feedback in each instance indicating whether the system correctly assessed the individual's emotional state in the case of the speaking instance. Once trained to classify emotional states, an expert system (optionally using another machine learning system or other artificial intelligence system) may be trained to optimize various vehicle parameters pointed out throughout this disclosure to maintain or induce a more favorable state based on feedback of the results of a set of individual emotional states. For example, if, among many other indicators, an individual's voice indicates happiness, the expert system may select or recommend upbeat music to maintain that state. If the voice indicates stress, the system may recommend or provide a control signal to change the planned route to a less stressful one (e.g., with less stop-and-go traffic or a higher probability of on-time arrival). In embodiments, the system may be configured to engage in a dialogue (such as an on-screen or voice dialogue) that is set up to help obtain feedback from the user about the user's emotional state, such as by using the system's intelligent agent module to ask the rider a series of questions about whether the rider is experiencing stress and what the source of that stress is (e.g., traffic conditions, likelihood of being late, probability of on-time arrival, etc.).For example, the system might ask the rider about sources of stress (such as traffic conditions, the possibility of delays, the behavior of other drivers, or other factors unrelated to the nature of the ride) and things that could reduce stress (such as route options, communication options (such as suggesting sending a note that there may be delays), entertainment options, and ride configuration options). The driver's responses are not only input into the expert system as an indicator of their emotional state, but may also be used to limit efforts to optimize one or more vehicle parameters, such as by excluding configuration options unrelated to the driver's sources of stress from the set of available configurations.

[0306] Embodiments provided herein include a transport system 3111 comprising: an artificial intelligence system 3136 for processing the voice 31135 of a lidar 3144 in a vehicle 3110 to determine the emotional state 3166 of the lidar 3144; and an embodiment for optimizing at least one operating parameter 31124 of the vehicle 3110 to improve the emotional state 3166 of the lidar 3144.

[0307] Embodiments provided herein are artificial intelligence systems 3136 for voice processing to improve rider satisfaction in a traffic system 3111, comprising: a rider voice capture system 30136 positioned to capture voice output 31128 of a rider 3144 riding in a vehicle 3110; a voice analysis circuit 31132 trained using machine learning to classify the rider's emotional state 31138 based on the captured rider's voice output; and an expert system 31139 using machine learning to optimize at least one operating parameter 31124 of the vehicle to change the rider's emotional state to an emotional state that is classified as an improved emotional state.

[0308] In an embodiment, the Rider voice capture system 31136 comprises an intelligent agent 31140 that interacts with the Rider to obtain Rider feedback for use by a voice analysis circuit 31132 for Rider emotional state classification. In an embodiment, the voice analysis circuit 31132 uses a first machine learning system, and the expert system 31139 uses a second machine learning system. In an embodiment, the expert system 31139 is trained to optimize at least one behavior parameter 31124 based on feedback of the resulting emotional state when adjusting at least one behavior parameter 31124 for a set of individuals. In an embodiment, the Rider's emotional state 3166 is determined by a combination of the Rider's captured voice output 31128 and at least one other parameter. In an embodiment, at least one other parameter is the Rider's camera-based emotional state determination. In an embodiment, at least one other parameter is traffic information. In an embodiment, at least one other parameter is weather information. In an embodiment, at least one other parameter is the vehicle state. In an embodiment, at least one other parameter is at least one pattern of lidar physiological data. In an embodiment, at least one other parameter is the vehicle's path. In an embodiment, at least one other parameter is in-vehicle audio content. In an embodiment, at least one other parameter is the vehicle's speed. In an embodiment, at least one other parameter is the vehicle's acceleration. In an embodiment, at least one other parameter is the vehicle's deceleration. In an embodiment, at least one other parameter is the proximity to objects along the path. In an embodiment, at least one other parameter is the proximity to other vehicles along the route.

[0309] Embodiments provided herein are artificial intelligence systems 3136 for speech processing to improve rider satisfaction, and include: a first neural network 3122, trained to classify emotional states based on analysis of human voices, detects the rider's emotional state through recognition of aspects of the rider's speech output 31128 captured while the rider is in a vehicle 3110 that correlate with at least one of the rider's emotional states 3166; and a second neural network 3120 optimizes the vehicle's driving parameters 31124 in accordance with the detected rider's emotional state 31126 to achieve a preferred emotional state for the rider 3142. In embodiments, at least one of the neural networks is a convolutional neural network. In embodiments, the first neural network 3122 is trained through the use of a training dataset that associates emotional state classes with human speech patterns. In embodiments, the first neural network 3122 is trained through the use of a training dataset of speech recordings tagged with emotional state identification data. In an embodiment, the Rider's emotional state is determined by a combination of the Rider's captured audio output and at least one other parameter. In an embodiment, at least one other parameter is the Rider's camera-based emotional state determination. In an embodiment, at least one other parameter is traffic information. In an embodiment, at least one other parameter is weather information. In an embodiment, at least one other parameter is the vehicle state.

[0310] In an embodiment, at least one other parameter is at least one pattern of lidar physiological data. In an embodiment, at least one other parameter is the vehicle's path. In an embodiment, at least one other parameter is in-vehicle audio content. In an embodiment, at least one other parameter is the vehicle's speed. In an embodiment, at least one other parameter is the vehicle's acceleration. In an embodiment, at least one other parameter is the vehicle's deceleration. In an embodiment, at least one other parameter is the proximity to objects along the path. In an embodiment, at least one other parameter is the proximity to other vehicles along the route.

[0311] Referring here to Figure 32, an embodiment provided herein is a transport system 3211 having an artificial intelligence system 3236 for processing data from the interaction between the LiDAR and the vehicle with an e-commerce system to determine the state of the LiDAR and for optimizing at least one operating parameter of the vehicle to improve the state of the LiDAR. Another common activity for the user of the device interface is e-commerce, such as shopping, bidding in auctions, and selling items. The e-commerce system uses search functions, undertakes advertising, and engages the user in various workflows that may ultimately lead to orders, purchases, bids, etc. As described herein regarding search, a set of in-vehicle-related search results may be provided for e-commerce, as well as in-vehicle-related advertising. Furthermore, the in-vehicle-related interface and workflow may be configured based on the detection of the in-vehicle LiDAR, which may be quite different from the workflow provided to the e-commerce interface configured for smartphones or desktop systems. Among other factors, the in-vehicle system may have access to information unavailable to conventional e-commerce systems, including route information (including direction, planned stops, and planned times), rider mood and behavior information (such as detected from past routes and from an in-vehicle sensor set), vehicle configuration and status information (such as manufacturer and model), and any of the other vehicle-related parameters described through this disclosure. For example, a rider who is bored (detected by an in-vehicle sensor set, such as using an expert system trained to detect boredom) and on a long journey (indicated by a route being taken by the vehicle) may be far more patient and likely to engage with deeper, richer content and longer workflows than a typical mobile user. Another example is that in-vehicle users may be far more likely to engage in free trials, surveys, or other actions that promote engagement with the brand. In addition, in-vehicle users may be able to use their time to achieve specific purposes, such as buying something they need.Presenting the same interface, content, and workflow to in-vehicle users may result in missing out on excellent opportunities for deeper engagement, which are less likely to occur in other environments where many elements compete to capture the user's attention. In embodiments, an e-commerce system interface may be provided to the in-vehicle user, and at least one of the interface display, content, search results, advertisements, and one or more associated workflows (such as for shopping, bidding, searching, purchasing, providing feedback, product display, rating, or review input) may be configured based on detection of the use of the in-vehicle interface. The display and interaction may be further configured based on the display type (e.g., enabling richer or larger images for a large HD display), network capabilities (e.g., enabling faster loading and lower latency by caching lower-resolution images to be rendered initially), audio system capabilities (e.g., using audio for conversational management and intelligent assistant interaction), and detection of similar ones for the vehicle (optionally based on a set of rules or based on machine learning). Display elements, content, and workflows may be configured by machine learning, such as the use of A / B testing and / or genetic programming techniques, including configuring alternative interaction types and tracking the results. The outcomes used to train the automated configuration of the in-vehicle e-commerce interface workflow may include engagement level, yield, purchases, rider satisfaction, ratings, and others. In-vehicle users may be profiled and clustered by behavioral profiling, demographic profiling, psychometric profiling, location-based profiling, collaborative filtering, similarity-based clustering, etc., as in conventional e-commerce, but the profiles may be extended by route information, vehicle information, vehicle configuration information, vehicle status information, rider information, etc.Sets of in-vehicle user profiles, groups, and clusters may be maintained separately from conventional user profiles to increase the likelihood that differences in in-vehicle shopping areas will be considered when targeting search results, advertisements, product offers, and discounts, thereby achieving learning about the content to be presented and how it should be presented.

[0312] Embodiments provided herein include a transport system 3211 comprising an artificial intelligence system 3236 for processing data from the interaction between a lidar 3244 and a vehicle's e-commerce system to determine the lidar state, and for optimizing at least one operating parameter of the vehicle to improve the lidar state.

[0313] Embodiments provided herein include a rider satisfaction system 32123 for optimizing rider satisfaction 32121, the rider satisfaction system comprising: an e-commerce interface 32141 deployed for access by a rider in a vehicle 3210; a rider dialogue circuit for capturing rider interactions with the deployed interface 32141; a rider state determination circuit 32143 for processing captured rider interactions 32144 to determine a rider state 32145; and an artificial intelligence system 3236 trained to optimize at least one parameter 32124 that affects the driving of the vehicle in order to improve the rider state 3237 in response to the rider state 3237. In embodiments, the vehicle 3210 includes a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle is at least a semi-autonomous vehicle. In embodiments, the vehicle is automatically routed. In embodiments, the vehicle is an autonomous driving vehicle. In the embodiment, the e-commerce interface is self-adaptive and responds to at least one of the following: rider identity, vehicle route, rider mood, rider behavior, vehicle configuration, and vehicle status.

[0314] In an embodiment, the e-commerce interface 32141 provides in-vehicle relevant content 32146 based on at least one of the following: rider identity, vehicle route, rider mood, rider behavior, vehicle configuration, and vehicle status. In an embodiment, the e-commerce interface executes a user interaction workflow 32147 adapted for use by the rider 3244 of the vehicle 3210. In an embodiment, the e-commerce interface provides one or more results of a search query 32148 adapted for presentation in the vehicle. In an embodiment, the results of the search query adapted for presentation in the vehicle are presented to the e-commerce interface along with advertisements adapted for presentation in the vehicle. In an embodiment, the rider interaction circuit 32142 captures rider interactions 32144 with the interface in response to the content 32146 presented to the interface.

[0315] Figure 33 shows a method 3300 for optimizing vehicle parameters according to embodiments of the systems and methods disclosed herein. In 3302, the method includes capturing lidar interactions in an in-vehicle e-commerce system. In 3304, the method includes determining a lidar state based on the captured lidar interactions and at least one operating parameter of the vehicle. In 3306, the method includes processing the lidar state with a lidar satisfaction model adapted to suggest at least one operating parameter of the vehicle that affects the lidar state. In 3306, the method includes optimizing the proposed at least one operating parameter for at least one of maintaining and improving the lidar state.

[0316] Refer to Figures 32 and 33. 33. Embodiments provided herein include an artificial intelligence system 3236 for improving rider satisfaction, comprising: a first neural network 3222 trained to classify rider states based on an analysis of rider interactions 32144 with an in-vehicle e-commerce system to detect rider states 32149 through recognition of aspects of rider interactions 32144 captured while the rider is in the vehicle that correlate with at least one rider state 3237; and a second neural network 3220 that optimizes vehicle driving parameters to achieve a rider-favorable state in response to the detected rider state.

[0317] Referring to Figure 34, embodiments provided herein include a transport system 3411 having an artificial intelligence system 3436 for processing data from at least one Internet of Things (IoT) device 34150 in the environment 34151 of the vehicle 3410 to determine the state of the vehicle 34152, and optimizing at least one operating parameter 34124 of the vehicle to improve the state of the LiDAR 3437 based on the determined state of the vehicle 34152.

[0318] Embodiments provided herein are transport systems 3411, characterized in that they include an artificial intelligence system 3436 that processes data from at least one IoT device 34150 in the environment 34151 of a vehicle 3410 to determine a determined state 34152 of the vehicle, and optimizes at least one operating parameter 34124 of the vehicle to improve the state 3437 of the lidar based on the determined state 34152 of the vehicle 3410.

[0319] Figure 35 shows a method 3500 for improving the state of a rider through optimization of vehicle operation, according to embodiments of the systems and methods disclosed herein. In 3502, the method includes capturing vehicle operation-related data with at least one IoT device. In 3504, the method includes analyzing the captured data with a first neural network that determines the state of the vehicle based at least in part on a portion of the captured vehicle operation-related data. In 3506, the method includes receiving data describing the state of a rider occupying a moving vehicle. In 3508, the method includes using a neural network to determine at least one vehicle operation parameter that affects the state of a rider riding in a driving vehicle. In 3508, the method includes using an artificial intelligence-based system to optimize at least one vehicle operation parameter such that the optimization result consists of an improvement in the state of the rider.

[0320] Referring to Figures 34 and 35, in an embodiment, the vehicle 3410 constitutes a system for automating at least one control parameter 34153 of the vehicle 3410. In an embodiment, the vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3410 is automatically routed. In an embodiment, the vehicle 3410 is an autonomous vehicle. In an embodiment, at least one IoT device 34150 is located in the vehicle's operating environment 34154. In an embodiment, at least one IoT device 34150 that captures data about the vehicle 3410 is located outside the vehicle 3410. In an embodiment, at least one IoT device is a dashboard camera. In an embodiment, at least one IoT device is a mirror camera. In an embodiment, at least one IoT device is a motion sensor. In an embodiment, at least one IoT device is a seat-based sensor system. In an embodiment, at least one IoT device is an IoT-enabled lighting system. In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device is a traffic signal camera or sensor. In an embodiment, at least one IoT device is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device is a road sensor. In an embodiment, at least one IoT device is an in-vehicle thermostat. In an embodiment, at least one IoT device is a toll booth. In an embodiment, at least one IoT device is a road sign. In an embodiment, at least one IoT device is a traffic control signal device. In an embodiment, at least one IoT device is a vehicle-mounted sensor. In an embodiment, at least one IoT device is a refueling system. In an embodiment, at least one IoT device is a recharging system. In an embodiment, at least one IoT device is a wireless charging station.

[0321] Embodiments provided herein include a rider state correction system 34155 for improving the state 3437 of a rider 3444 in a vehicle 3410, the system comprising: a first neural network 3422 that operates to classify the state of a vehicle through analysis of information about the vehicle taken in by an Internet of Things device 34150 while the vehicle 3410 is in operation; and a second neural network 3420 that operates to optimize at least one operating parameter 34124 of the vehicle based on the classified state of the vehicle 34152, information about the state of a rider riding in the vehicle, and information correlating the vehicle's operation with its effect on the rider state.

[0322] In an embodiment, the vehicle constitutes a system for automating at least one control parameter 34153 of the vehicle 3410. In an embodiment, the vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3410 is automatically routed. In an embodiment, the vehicle 3410 is an autonomous vehicle. In an embodiment, at least one Internet of Things device 34150 is located within the operating environment of the vehicle 3410. In an embodiment, at least one IoT device 34150 that captures data about the vehicle 3410 is located outside the vehicle 3410. In an embodiment, at least one IoT device is a dashboard camera. In an embodiment, at least one IoT device is a mirror camera. In an embodiment, at least one IoT device is a motion sensor. In an embodiment, at least one IoT device is a seat-based sensor system. In an embodiment, at least one IoT device is an IoT-enabled lighting system.

[0323] In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device is a traffic signal camera or sensor. In an embodiment, at least one IoT device is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device is a road sensor. In an embodiment, at least one IoT device is an on-board thermostat. In an embodiment, at least one IoT device is a toll booth. In an embodiment, at least one IoT device is a road sign. In an embodiment, at least one IoT device is a traffic control signal device. In an embodiment, at least one IoT device is a vehicle-mounted sensor. In an embodiment, at least one IoT device is a refueling system. In an embodiment, at least one IoT device is a recharging system. In an embodiment, at least one IoT device is a wireless charging station.

[0324] Embodiments provided herein include an artificial intelligence system 3436 comprising: a first neural network 3422 trained to determine the operating state 34152 of a vehicle 3410 from data about the vehicle captured in the vehicle's operating environment 34154, the first neural network 3422 operating to identify the operating state 34152 of the vehicle by processing information about the vehicle 3410 captured by at least one Internet of Things device 34150 while the vehicle is operating; a data structure 34156 that facilitates the determination of operating parameters that affect the operating state of the vehicle; and a second neural network 3420 operating to optimize at least one of the determined operating parameters 34124 of the vehicle based on the identified operating state 34152 by processing information about the state of a rider 3444 riding in the vehicle 3410, and information that correlates the vehicle's operation with the rider's state.

[0325] In an embodiment, improvements in the lidar state are reflected in update data describing the lidar state captured in response to vehicle operation based on an optimized vehicle operation parameter. In an embodiment, improvements in the lidar state are reflected in data captured by at least one IoT device 34150, which is configured to capture information about the lidar 3444 while occupying vehicle 3410 in response to optimization. In an embodiment, vehicle 3410 constitutes a system for automating at least one control parameter 34153 of the vehicle. In an embodiment, vehicle 3410 is at least a semi-autonomous vehicle. In an embodiment, vehicle 3410 is automatically routed. In an embodiment, vehicle 3410 is an autonomous vehicle. In an embodiment, at least one IoT device 34150 is located in the vehicle's operating environment 34154. In an embodiment, at least one IoT device 34150 that captures data about the vehicle is located outside the vehicle. In an embodiment, at least one IoT device 34150 is a dashboard camera. In one embodiment, at least one IoT device 34150 is a mirror camera. In another embodiment, at least one IoT device 34150 is a motion sensor. In another embodiment, at least one IoT device 34150 is a sheet-based sensor system. In another embodiment, at least one IoT device 34150 is an IoT-enabled lighting system.

[0326] In an embodiment, the lighting system is an interior lighting system. In an embodiment, the lighting system is a headlight lighting system. In an embodiment, at least one IoT device 34150 is a traffic signal camera or sensor. In an embodiment, at least one IoT device 34150 is a road camera. In an embodiment, the road surface camera is located on at least one of a telephone and a utility pole. In an embodiment, at least one IoT device 34150 is a road sensor. In an embodiment, at least one IoT device 34150 is an on-board thermostat. In an embodiment, at least one IoT device 34150 is a toll booth. In an embodiment, at least one IoT device 34150 is a road sign. In an embodiment, at least one IoT device 34150 is a traffic control signal device. In an embodiment, at least one IoT device 34150 is a vehicle-mounted sensor. In an embodiment, at least one IoT device 34150 is a refueling system. In an embodiment, at least one IoT device 34150 is a recharging system. In one embodiment, at least one IoT device 34150 is a wireless charging station.

[0327] Referring to Figure 36, an embodiment provided herein is a transport system 3611 having an artificial intelligence system 3636 for processing sensory input from a wearable device 36157 in a vehicle 3610 to determine an emotional state 36126 and for optimizing at least one operating parameter 36124 of the vehicle 3610 to improve the rider's emotional state 3637. A wearable device 36150, such as any described throughout this disclosure, may be used both as an input to a real-time control system (such as any type of model-based, rule-based, or artificial intelligence system described herein) to detect any of the emotional states described herein (favorable or unfavorable) and to indicate the purpose of improving an unfavorable state or maintaining a favorable state, and as a feedback mechanism for training the artificial intelligence system 3636 to configure a set of operating parameters 36124 to promote or maintain a favorable state.

[0328] Embodiments provided herein include a transport system 3611 comprising: an artificial intelligence system 3636 for processing sensory input from a wearable device 36157 in a vehicle 3610 to determine the emotional state 36126 of a lidar 3644 in the vehicle 3610; and a system for optimizing vehicle driving parameters 36124 to improve the emotional state 3637 of the lidar 3644. In embodiments, the vehicle is an autonomous vehicle. In embodiments, the artificial intelligence system 3636 detects the emotional state 36126 of a lidar riding in an autonomous vehicle by recognizing a pattern of emotional state indication data from a pair of wearable sensors 36157 worn by the lidar 3644. In embodiments, the pattern indicates at least one of the lidar's preferred emotional state and the lidar's unpredictable emotional state. In an embodiment, the artificial intelligence system 3636 optimizes the vehicle's operating parameters 36124 in response to the detected emotional state of the rider in order to achieve at least one of the following: maintaining the rider's detected favorable emotional state, and achieving the rider's favorable emotional state following the detection of an unfavorable emotional state. In an embodiment, the artificial intelligence system 3636 includes an expert system that detects the rider's emotional state by processing rider emotional state instruction data received from a set of wearable sensors 36157 worn by the rider. In an embodiment, the expert system processes rider emotional state index data using at least one of a training set of emotional state indices for a set of riders and a rider emotional state index generated by a trainer. In an embodiment, the artificial intelligence system includes a recurrent neural network 3622 that detects the rider's emotional state.

[0329] In an embodiment, the recurrent neural network consists of multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the artificial intelligence system 3636 comprises a radial basis function neural network that optimizes the operation parameters 36124. In an embodiment, optimizing the operation parameters 36124 is based on a correlation between the vehicle operation state 3645 and the rider emotion state 3637. In an embodiment, the correlation is determined using a training set of emotion state indices for a set of riders and at least one of the rider emotion state indices generated by a human trainer. In an embodiment, the vehicle operation parameters to be optimized are determined and adjusted to induce a preferred rider emotion state.

[0330] In an embodiment, the artificial intelligence system 3636 further learns to classify patterns of emotional state index data from a training dataset 36131 supplied from at least one of the data flows from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice system, and to associate patterns with emotional states and their changes. In an embodiment, the artificial intelligence system 3636 detects patterns of rider emotional state indication data that indicate the rider's emotional state is changing from a first emotional state to a second emotional state, and the optimization of vehicle driving parameters is in response to the indicated change in emotional state. In an embodiment, patterns of rider emotional state indication data indicate that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an unfavorable state and the rider's emotional state is changing to a favorable state.

[0331] In an embodiment, the operational parameter 36124 to be optimized affects at least one of the vehicle's route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In an embodiment, the artificial intelligence system 3636 interacts with the vehicle control system to optimize the operational parameter. In an embodiment, the artificial intelligence system 3636 further includes a neural network 3622 including one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, the set of wearable sensors 36157 includes at least two of the following: watches, rings, wristbands, armbands, ankle bands, torso bands, skin patches, head-mounted devices, glasses, footwear, gloves, in-ear devices, clothing, headphones, belts, rings, thumb rings, and necklaces. In an embodiment, the artificial intelligence system 3636 uses deep learning to determine patterns of wearable sensor-generated emotional state indication data that indicate the lidar's emotional state as at least one of a favorable emotional state and an unfavorable emotional state. In an embodiment, the artificial intelligence system 3636 responds to the emotional state indicated by the lidar by optimizing operational parameters to at least one of achieving and maintaining the emotional state indicated by the lidar.

[0332] In an embodiment, the artificial intelligence system 3636 adapts the characterization of the rider's preferred emotional state based on context collected from multiple sources, including data indicating the rider's purpose for riding in the autonomous vehicle, time of day, traffic conditions, and weather, and optimizes the operating parameters 36124 for at least one of achieving and maintaining the adapted preferred emotional state. In an embodiment, the artificial intelligence system 3636 optimizes the operating parameters in real time in response to the detection of the rider's emotional state. In an embodiment, the vehicle is an autonomous vehicle. In an embodiment, the artificial intelligence system comprises: a first neural network 3622 that detects the rider's emotional state through expert system-based processing of rider emotional state index wearable sensor data from multiple wearable physiological state sensors attached to the vehicle, wherein the emotional state index wearable sensor data indicates at least one of the rider's preferred emotional state and the rider's unpredictable emotional state; and a second neural network 3620 that optimizes the vehicle's operating parameters 36124 in response to the detected emotional state of the rider, for at least one of achieving and maintaining the rider's preferred emotional state. In this embodiment, the first neural network 3622 is a recurrent neural network, and the second neural network 3620 is a radial basis function neural network.

[0333] In an embodiment, the second neural network 3620 optimizes the operation parameters 36124 based on the correlation between the vehicle operating state 3645 and the rider emotional state 3637. In an embodiment, the vehicle operation parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the first neural network 3622 further learns to classify patterns in rider emotional state indicative wearable sensor data from a training dataset supplied from at least one of the data flows from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice system, and to associate the patterns with emotional states and their changes. In an embodiment, the second neural network 3620 optimizes the operation parameters in real time in response to the detection of the rider's emotional state by the first neural network 3622. In an embodiment, the first neural network 3622 detects patterns in rider emotional state wearable sensor data indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In this embodiment, the second neural network 3620 optimizes the vehicle's operating parameters in response to the indicated changes in emotional state.

[0334] In an embodiment, the first neural network 3622 comprises a plurality of connected nodes forming a directed cycle, and the first neural network 3622 further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the first neural network 3622 includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, wearable sensor data indicating the rider's emotional state indicates that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state and the rider's emotional state is changing to a favorable state. In an embodiment, the driving parameters to be optimized affect at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, proximity to objects along the path, and proximity to other vehicles along the path. In an embodiment, the second neural network 3620 interacts with the vehicle control system to adjust the operating parameters. In an embodiment, the first neural network 3622 includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0335] In the embodiment, the vehicle is an autonomous vehicle. In the embodiment, the artificial intelligence system 3636 detects, at least partially, a change in the emotional state of a rider riding in an autonomous vehicle by recognizing a pattern of emotional state indication data from a set of wearable sensors worn by the rider. In the embodiment, the pattern indicates at least one of a decline in the rider's preferred emotional state and the onset of the rider's undesirable emotional state. In the embodiment, the artificial intelligence system 3636 determines at least one operating parameter 36124 of the autonomous vehicle that indicates the change in emotional state, based on the correlation between the pattern of emotional state data and a set of vehicle operating parameters. In the embodiment, the artificial intelligence system 3636 determines to adjust at least one operating parameter 36124 to achieve at least one of restoring the rider's preferred emotional state and reducing the onset of the rider's undesirable emotional state.

[0336] In embodiments, the correlation of patterns in rider emotional state index wearable sensor data is determined using a training set of emotional state wearable sensor indices for a set of riders and at least one of rider emotional state wearable sensor indices generated by a human trainer. In embodiments, the artificial intelligence system 3636 further learns to classify patterns in emotional state index wearable sensor data from a training dataset supplied from at least one of data flows from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice system, and to associate the patterns with changes in rider emotional state. In embodiments, patterns in wearable sensor data indicating a rider's emotional state indicate that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, the rider's emotional state is changing to an unfavorable state, and the rider's emotional state is changing to a favorable state.

[0337] In embodiments, operational parameters determined from the results of processing wearable sensor data indicating the rider's emotional state affect at least one of the following: the vehicle's route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, the artificial intelligence system 3636 further interacts with the vehicle control system to adjust the operational parameters. In embodiments, the artificial intelligence system 3636 further includes a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0338] In an embodiment, the set of wearable sensors includes at least two of the following: a watch, a ring, a wristband, an armband, ankle band, a torso band, a skin patch, a head-mounted device, eyeglasses, footwear, gloves, an ear-hook device, clothing, headphones, a belt, a finger ring, a thumb ring, and a necklace. In an embodiment, the artificial intelligence system 3636 uses deep learning to determine patterns in wearable sensor-generated emotional state indication data that indicate changes in the rider's emotional state. In an embodiment, the artificial intelligence system 3636 further determines changes in the rider's emotional state based on context collected from multiple sources, including data indicating the purpose of the rider riding in the autonomous vehicle, the time of day, traffic conditions, and weather, and optimizes the operation parameters 36124 to achieve and maintain a fitted, preferred emotional state. In an embodiment, the artificial intelligence system 3636 adjusts the operation parameters in real time in response to the detection of changes in the rider's emotional state.

[0339] In an embodiment, the vehicle is an autonomous vehicle. In an embodiment, the artificial intelligence system 3636 includes a recurrent neural network for indicating changes in the emotional state of a lidar in an autonomous vehicle by recognizing patterns of wearable sensor data indicating emotional states from a set of wearable sensors worn by the lidar. In an embodiment, the patterns indicate at least one of a first degree of the lidar's preferred emotional state and a second degree of the lidar's unfavorable emotional state, and the system includes a radial basis function neural network that optimizes the vehicle's operating parameters 36124 to achieve a target emotional state for the lidar in response to instructions for changes in the lidar's emotional state.

[0340] In an embodiment, a radial basis function neural network optimizes operating parameters based on a correlation between the vehicle's operating state and the rider's emotional state. In an embodiment, the target emotional state is a preferred rider emotional state, and the vehicle driving parameters to be optimized are determined and adjusted to induce the preferred rider emotional state. In an embodiment, a recurrent neural network further learns to classify patterns in wearable sensor data indicating emotional states and associate them with changes in emotional states, from a training dataset supplied from at least one of the following data streams: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems. In an embodiment, the radial basis function neural network optimizes operating parameters in real time in response to the recurrent neural network's detection of changes in the rider's emotional state. In an embodiment, the recurrent neural network detects patterns in emotional state index wearable sensor data indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the radial basis function neural network optimizes the vehicle's operating parameters in response to the indicated change in emotional state. In one embodiment, the recurrent neural network comprises multiple connected nodes that form a directed cycle, and the recurrent neural network further facilitates the bidirectional flow of data between the connected nodes.

[0341] In embodiments, patterns of wearable sensor data indicating emotional state indicate that the rider's emotional state is changing, stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, indicating that the rider's emotional state is changing to an undesirable state and that the rider's emotional state is changing to a favorable state. In embodiments, the operational parameters to be optimized affect at least one of the following parameters: vehicle route, in-vehicle audio content, vehicle speed, vehicle acceleration, vehicle deceleration, proximity to objects along the route, and proximity to other vehicles along the route. In embodiments, a radial basis function neural network interacts with the vehicle control system to tune the operational parameters. In embodiments, a recurrent neural network includes one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0342] In one embodiment, the artificial intelligence system 3636 maintains the rider's preferred emotional state by using a modular neural network, the modular neural network comprising a rider emotional state determination neural network that processes wearable sensor data indicating the rider's emotional state in the vehicle and detects patterns. In another embodiment, the system comprises an intermediary circuit that converts output data from the rider emotional state determination neural network into vehicle operating state data, where patterns found in the emotional state index wearable sensor data indicate at least one of the rider's preferred emotional state and the rider's unfavorable emotional state, and a vehicle operating state optimization neural network that adjusts the vehicle's operating parameters 36124 in response to the vehicle operating state data.

[0343] In an embodiment, the vehicle driving state optimization neural network adjusts the vehicle driving parameters to achieve a preferred emotional state for the rider. In an embodiment, the vehicle driving state optimization neural network optimizes the driving parameters based on the correlation between the vehicle driving state and the rider emotional state. In an embodiment, the vehicle driving parameters to be optimized are determined and adjusted to induce a preferred rider emotional state. In an embodiment, the rider emotional state determination neural network further learns to classify patterns in wearable sensor data indicating emotional states from a training dataset supplied from at least one of the following data flows: unstructured data sources, social media sources, wearable devices, in-vehicle sensors, rider helmets, rider headgear, and rider voice systems, and associate them with emotional states and their changes.

[0344] In an embodiment, the vehicle driving state optimization neural network optimizes driving parameters in real time in response to the detection of changes in the rider's emotional state by the rider emotional state determination neural network. In an embodiment, the rider emotional state determination neural network detects patterns in emotional state indicative wearable sensor data indicating that the rider's emotional state is changing from a first emotional state to a second emotional state. In an embodiment, the vehicle operating state optimization neural network optimizes the vehicle's operating parameters in response to the indicated change in emotional state. In an embodiment, the artificial intelligence system 3636 comprises a plurality of connected nodes that form a directed cycle, and the artificial intelligence system 3636 further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, the patterns in wearable sensor data indicating an emotional state indicate that the rider's emotional state is changing, the rider's emotional state is stable, the rate of change in the rider's emotional state, the direction of change in the rider's emotional state, and the polarity of change in the rider's emotional state, the rider's emotional state is changing to an undesirable state, and the rider's emotional state is changing to a favorable state.

[0345] In an embodiment, the operational parameters to be optimized affect at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, the proximity to objects along the path, and the proximity to other vehicles along the path. In an embodiment, the vehicle driving state optimization neural network interacts with the vehicle control system to adjust the driving parameters. In an embodiment, the artificial intelligence system 3636 further includes a neural network including one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In an embodiment, the rider emotional state determination neural network comprises one or more perceptrons that mimic human sensations, which facilitates determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated.

[0346] In an embodiment, the artificial intelligence system 3636 indicates changes in the emotional state of a rider in a vehicle by recognizing patterns in wearable sensor data indicating the emotional state of the rider in the vehicle, and the transport system further comprises a vehicle control system that controls the operation of the vehicle by adjusting a plurality of vehicle operation parameters, and a feedback loop in which instructions for changes in the rider's emotional state are transmitted between the vehicle control system and the artificial intelligence system 3636. In an embodiment, the vehicle control system adjusts at least one of the plurality of vehicle operation parameters in response to instructions for change. In an embodiment, the vehicle control system adjusts at least one of the plurality of vehicle operation parameters based on a correlation between the vehicle operation state and the rider's emotional state.

[0347] In an embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters that indicate a desirable rider emotional state. In an embodiment, the vehicle control system selects at least one of a plurality of vehicle operation parameters that indicate the generation of a desirable rider emotional state. In an embodiment, the artificial intelligence system 3636 further learns to classify patterns of wearable sensor data indicating emotional states and associate them with changes in emotional states from a training dataset supplied from at least one of the data flows from an unstructured data source, a social media source, a wearable device, an in-vehicle sensor, a rider helmet, a rider headgear, and a rider voice system. In an embodiment, the vehicle control system adjusts at least one of a plurality of vehicle operation parameters in real time.

[0348] In an embodiment, the artificial intelligence system 3636 further detects patterns in emotion state indicative wearable sensor data indicating that the rider's emotion state is changing from a first emotion state to a second emotion state. In an embodiment, the vehicle driving control system adjusts the vehicle's driving parameters in response to the indicated change in emotion state. In an embodiment, the artificial intelligence system 3636 comprises a plurality of connected nodes that form a directed cycle, and the artificial intelligence system 3636 further facilitates the bidirectional flow of data between the connected nodes. In an embodiment, at least one of a plurality of vehicle operating parameters that are adjusted responsively affects the operation of the vehicle's powertrain and the vehicle's suspension system.

[0349] In one embodiment, a radial-based function neural network interacts with a recurrent neural network via an intermediate component of an artificial intelligence system 3636 that generates vehicle control data indicating the rider's emotional state response to the vehicle's current operating state. In another embodiment, the artificial intelligence system 3636 further comprises a modular neural network including a rider emotional state recurrent neural network for indicating changes in the rider's emotional state, a vehicle operating state radial-based function neural network, and an intermediary system. In yet another embodiment, the intermediary system processes the rider emotional state characterization data from the recurrent neural network into vehicle control data that the radial-based function neural network uses to interact with the vehicle control system to adjust at least one operating parameter.

[0350] In one embodiment, the artificial intelligence system 3636 comprises a neural network including one or more perceptrons that mimic human sensations to facilitate determining the rider's emotional state based on the degree to which at least one of the rider's senses is stimulated. In another embodiment, the recognition of patterns in wearable sensor data indicating emotional states comprises processing wearable sensor data indicating emotional states that has been captured before adjusting at least one of a plurality of vehicle operating parameters, during adjusting at least one of the plurality of vehicle operating parameters, and during at least two periods after adjusting at least one of the plurality of vehicle operating parameters.

[0351] In one embodiment, the artificial intelligence system 3636 indicates changes in the rider's emotional state in response to changes in the vehicle's operating parameters 36124 by determining the difference between a first set of wearable sensor data indicating the rider's emotional state captured before adjusting at least one of the multiple operating parameters and a second set of wearable sensor data indicating the rider's emotional state captured during or after adjusting at least one of the multiple operating parameters.

[0352] Referring to Figure 37, an embodiment provided herein provides a transport system 3711 having a cognitive system 37158 for managing an advertising market for in-seat advertising for a lidar 3744 of an autonomous vehicle. In an embodiment, the cognitive system 37158 takes input related to at least one parameter 37124 of the vehicle and / or lidar 3744 in order to determine at least one of the price, type, and location of an advertisement to be delivered within interface 3713 to the lidar 3744 of a seat 3728 of the vehicle. As described above in relation to search, in-vehicle lidars, in particular lidars of autonomous vehicles, may be situationally positioned to advertisements when they are in the vehicle, in a way that is entirely different from when they are otherwise. A bored lidar may be more proactive in viewing advertising content, clicking on offers or promotions, engaging in research, etc. In an embodiment, the advertising marketplace platform may segment and process advertising placements for in-vehicle advertising (including processing such as bidding and soliciting for advertising placements) separately. Such an advertising marketplace platform may use vehicle-specific information, such as vehicle type, display type, audio system capabilities, screen size, rider demographics, route information, and location information, when characterizing ad placement opportunities, so that bids for in-vehicle ad placements reflect such vehicles, riders, and other traffic-related parameters. For example, an advertiser could bid on an ad placement on the in-vehicle display system of an autonomous vehicle valued at over $50,000 and routed north on Highway 101 during morning rush hour. The advertising marketplace platform may configure many such vehicle-related placement opportunities, process bids for such opportunities, place ads (e.g., by load-balanced servers that cache ads), and resolve results. Yield metrics may be tracked and used to optimize the configuration of the marketplace.

[0353] Embodiments provided herein are systems for transport, comprising a cognitive system 37158 for managing an advertising market for in-seat advertisements for LiDAR of an autonomous vehicle, wherein the cognitive system 37158 takes an input corresponding to at least one parameter 37159 of the vehicle or LiDAR 3744 to determine the characteristics 37160 of an advertisement to be delivered within an interface 3713 to LiDAR 3744 of a seat 3728 of the vehicle, and the characteristics 37160 of the advertisement are selected from a group consisting of price, category, location and combinations thereof.

[0354] Figure 38 shows a method 3800 for in-vehicle seat advertising according to embodiments of the systems and methods disclosed herein. In 3802, the method includes taking an input relating to at least one parameter of the vehicle. In 3804, the method includes taking an input relating to at least one parameter of a rider occupying the vehicle. In 3802, the method includes determining at least one of the price, classification, content, and location of an advertisement to be delivered within the vehicle interface to a rider in a seat of the vehicle, based on the vehicle-related input and the rider-related input.

[0355] Referring to Figures 37 and 38, in the embodiment, the vehicle 3710 is automatically routed. In the embodiment, the vehicle 3710 is an autonomous vehicle. In the embodiment, the cognitive system 37158 further determines at least one of the price, classification, content, and location of the advertisement placement. In the embodiment, the advertisement is delivered by the winning advertiser. In the embodiment, the delivery of the advertisement is based on the winning bid price. In the embodiment, the input 37162 related to at least one parameter of the vehicle includes the vehicle classification. In the embodiment, the input 37162 related to at least one parameter of the vehicle includes the display classification. In the embodiment, the input 37162 related to at least one parameter of the vehicle includes the audio system capability. In the embodiment, the input 37162 related to at least one parameter of the vehicle includes the screen size.

[0356] In an embodiment, input 37162 related to at least one parameter of the vehicle includes route information. In an embodiment, input 37162 related to at least one parameter of the vehicle includes location information. In an embodiment, input 37163 related to at least one parameter of the rider includes rider demographic information. In an embodiment, input 37163 related to at least one parameter of the rider includes the rider's emotional state. In an embodiment, input 37163 related to at least one parameter of the rider includes the rider's response to pre-seat advertisements. In an embodiment, input 37163 related to at least one parameter of the rider includes the rider's social media activity.

[0357] Figure 39 shows a method 3900 for tracking in-vehicle advertising interactions according to embodiments of the systems and methods disclosed herein. In 3902, the method includes taking inputs related to at least one parameter of a vehicle and inputs related to at least one parameter of a rider occupying the vehicle. In 3904, the method includes aggregating inputs across multiple vehicles. In 3906, the method includes using a cognitive system to determine in-vehicle advertising placement opportunities based on the aggregated inputs. In 3907, the method includes providing placement opportunities in an advertising network that facilitates bidding for placement opportunities. In 3908, the method includes delivering advertisements for placement within the vehicle's user interface based on the results of the bidding. In 3909, the method includes monitoring interactions between advertisements presented in the vehicle's user interface and the vehicle's rider.

[0358] Referring to Figures 37 and 39, in an embodiment, vehicle 3710 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, vehicle 3710 is at least a semi-autonomous vehicle. In an embodiment, vehicle 3710 is automatically routed. In an embodiment, vehicle 3710 is an autonomous vehicle. In an embodiment, advertisements are delivered by the winning advertiser. In an embodiment, advertisement delivery is based on the winning bid amount. In an embodiment, monitored vehicle lidar interaction information includes information for resolving click-based payments. In an embodiment, monitored vehicle lidar interaction information includes the results of monitoring analysis. In an embodiment, the analysis results are a measure of interest in the advertisement. In an embodiment, input 37162 related to at least one parameter of the vehicle includes vehicle classification.

[0359] In an embodiment, input 37162 related to at least one parameter of the vehicle includes display classification. In an embodiment, input 37162 related to at least one parameter of the vehicle includes audio system capability. In an embodiment, input 37162 related to at least one parameter of the vehicle includes screen size. In an embodiment, input 37162 related to at least one parameter of the vehicle includes route information. In an embodiment, input 37162 related to at least one parameter of the vehicle includes location information. In an embodiment, input 37163 related to at least one parameter of the rider includes rider demographic information. In an embodiment, input 37163 related to at least one parameter of the rider includes rider emotional state. In an embodiment, input 37163 related to at least one parameter of the rider includes rider's reaction to pre-seat advertising. In an embodiment, input 37163 related to at least one parameter of the rider includes rider's social media activity.

[0360] Figure 40 shows a method 4000 for in-vehicle advertising according to embodiments of the systems and methods disclosed herein. In 4002, the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a lidar occupying the vehicle. In 4004, the method includes aggregating inputs across multiple vehicles. In 4006, the method includes using a cognitive system to determine in-vehicle advertising placement opportunities based on the aggregated inputs. In 4008, the method includes providing placement opportunities in an advertising network that facilitates bidding for placement opportunities. In 4009, the method includes delivering advertisements for placement within the vehicle interface based on the results of the bidding.

[0361] Referring to Figures 37 and 40, in an embodiment, the vehicle 3710 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 3710 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 3710 is automatically routed. In an embodiment, the vehicle 3710 is an autonomous vehicle. In an embodiment, the perception system 37158 further determines at least one of the price, classification, content, and location of the advertisement placement. In an embodiment, the advertisement is delivered by the winning advertiser. In an embodiment, the delivery of the advertisement is based on the winning bid price. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes the vehicle classification.

[0362] In an embodiment, input 37162 related to at least one parameter of the vehicle includes display classification. In an embodiment, input 37162 related to at least one parameter of the vehicle includes audio system capability. In an embodiment, input 37162 related to at least one parameter of the vehicle includes screen size. In an embodiment, input 37162 related to at least one parameter of the vehicle includes route information. In an embodiment, input 37162 related to at least one parameter of the vehicle includes location information. In an embodiment, input 37163 related to at least one parameter of the rider includes rider demographic information. In an embodiment, input 37163 related to at least one parameter of the rider includes rider emotional state. In an embodiment, input 37163 related to at least one parameter of the rider includes rider response to prior in-seat advertising. In an embodiment, input 37163 related to at least one parameter of the rider includes rider social media activity.

[0363] Embodiments provided herein include an advertising system for in-vehicle seat advertising, the advertising system comprising: a cognitive system 37158 that takes an input 37162 relating to at least one parameter 37124 of a vehicle 3710 and an input 37161 relating to at least one parameter of a rider riding in the vehicle; and a system 3714 that determines at least one of the price, classification, content and location of an advertisement to be delivered within the interface 37133 of the vehicle 3710 to the rider 3744 of a seat 3728 of the vehicle 3710 based on the vehicle-related input 37162 and the rider-related input 37163.

[0364] In an embodiment, the vehicle 4110 includes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 4110 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 4110 is automatically routed. In an embodiment, the vehicle 4110 is an autonomous vehicle. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes vehicle classification. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes display classification. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes audio system capability. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes screen size. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes route information. In an embodiment, the input 37162 related to at least one parameter of the vehicle includes location information. In an embodiment, the input 37163 related to at least one parameter of the LiDAR includes LiDAR demographic information. In an embodiment, the input 37163 related to at least one parameter of the rider includes the rider's emotional state. In an embodiment, the input 37163 related to at least one parameter of the rider includes the rider's reaction to pre-seat advertising. In an embodiment, the input 37163 related to at least one parameter of the rider includes the rider's social media activity.

[0365] In an embodiment, the advertising system further comprises determining a vehicle operating state from an input 37162 related to at least one parameter of the vehicle. In an embodiment, the advertisement to be delivered is determined at least in part on the determined vehicle operating state. In an embodiment, the advertising system further comprises determining a lidar state 37149 from an input 37163 related to at least one parameter of the lidar. In an embodiment, the advertisement to be delivered is determined at least in part on the determined lidar state 37149.

[0366] Referring to Figure 41, an embodiment provided herein is a transport system 4111 having a hybrid recognition system 41164 for managing an advertising market for in-seat advertising to a rider in a vehicle 4110. In the embodiment, at least a portion of the hybrid recognition system 41164 processes an input 41162 relating to at least one parameter 41124 of the vehicle to determine the vehicle operating state, and at least another portion of the recognition system processes an input relating to the rider to determine the rider state. In the embodiment, the recognition system determines at least one of the price, type, and location of an advertisement delivered within the interface to a rider in a seat of the vehicle.

[0367] Embodiments provided herein are characterized by a transport system 4111 comprising a hybrid recognition system 41164 for managing an advertising market for in-seat advertising to a rider 4144 of a vehicle 4110. In embodiments, at least one part 41165 of the hybrid recognition system processes an input 41162 corresponding to at least one parameter of the vehicle to determine a vehicle operating state 41168, and at least one other part 41166 of the recognition system 41164 processes an input 41163 related to a rider to determine a rider state 41149. In embodiments, the recognition system 41164 determines advertising characteristics 41160 to be delivered within interface 41133 to a rider 4144 in a seat 4128 of the vehicle 4110. In embodiments, advertising characteristics 41160 are selected from the group consisting of price, category, location, and combinations thereof.

[0368] Embodiments provided herein include an artificial intelligence system 4136 for in-seat advertising in a vehicle, which comprises: a first part 41165 of the artificial intelligence system 4136 that determines the operating state 41168 of a vehicle by processing an input 41162 related to at least one parameter of the vehicle; a second part 41166 of the artificial intelligence system 4136 that determines the rider state 41149 of the vehicle by processing an input 41163 related to at least one parameter of the rider; and a third part 41167 of the artificial intelligence system 4136 that determines at least one of the price, classification, content and location of an advertisement to be delivered within the vehicle interface 41133 to a rider 4144 in a seat of the vehicle 4110, based on the vehicle (operating) state 41168 and the rider state 41149.

[0369] In an embodiment, the vehicle 4110 includes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle is at least a semi-autonomous vehicle. In an embodiment, the vehicle is automatically routed. In an embodiment, the vehicle is an autonomous vehicle. In an embodiment, the perception system 41164 further determines at least one of the price, classification, content, and location of the advertisement placement. In an embodiment, the advertisement is delivered by the winning advertiser. In an embodiment, the delivery of the advertisement is based on the winning bid price. In an embodiment, the input related to at least one parameter of the vehicle includes the vehicle classification.

[0370] In an embodiment, the input related to at least one parameter of the vehicle includes display classification. In an embodiment, the input related to at least one parameter of the vehicle includes audio system capability. In an embodiment, the input related to at least one parameter of the vehicle includes screen size. In an embodiment, the input related to at least one parameter of the vehicle includes route information. In an embodiment, the input related to at least one parameter of the vehicle includes location information. In an embodiment, the input related to at least one parameter of the rider includes rider demographic information. In an embodiment, the input related to at least one parameter of the rider includes rider emotional state. In an embodiment, the input related to at least one parameter of the rider includes rider's reaction to pre-seat advertising. In an embodiment, the input related to at least one parameter of the rider includes rider's social media activity.

[0371] Figure 42 shows a method 4200 for tracking in-vehicle advertising interactions according to embodiments of the systems and methods disclosed herein. In 4202, the method includes taking inputs relating to at least one parameter of a vehicle and inputs relating to at least one parameter of a lidar occupying the vehicle. In 4204, the method includes aggregating inputs across multiple vehicles. In 4206, the method includes using a hybrid cognitive system to determine in-vehicle advertising placement opportunities based on the aggregated inputs. In 4207, the method includes providing placement opportunities in an advertising network that facilitates bidding for placement opportunities. In 4208, the method includes delivering advertisements for placement within the vehicle's user interface based on the results of the bidding. In 4209, the method includes monitoring interactions between advertisements presented within the vehicle's user interface and the vehicle lidar.

[0372] Referring to Figures 41 and 42, in an embodiment, the vehicle 4110 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 4110 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 4110 is automatically routed. In an embodiment, the vehicle 4110 is an autonomous vehicle. In an embodiment, a first part 41165 of the hybrid perception system 41164 determines the operating state of the vehicle by processing inputs related to at least one parameter of the vehicle. In an embodiment, a second part 41166 of the hybrid perception system 41164 determines the state of the vehicle's rider 41149 by processing inputs related to at least one parameter of the rider. In an embodiment, a third part 41167 of the hybrid perception system 41164 determines at least one of the price, category, content, and location of an advertisement to be delivered within the vehicle interface to the rider in the vehicle's seat, based on the vehicle state and the rider state. In an embodiment, the advertisement is delivered by the winning advertiser. In an embodiment, the delivery of the advertisement is based on the winning bid. In an embodiment, the monitored vehicle-rider interaction information includes information for resolving click-based payments. In an embodiment, the monitored vehicle-rider interaction information includes the results of the monitoring analysis. In an embodiment, the analysis results are a measure of interest in advertising. In an embodiment, input 41162 related to at least one parameter of the vehicle includes vehicle classification. In an embodiment, input 41162 related to at least one parameter of the vehicle includes display classification. In an embodiment, input 41162 related to at least one parameter of the vehicle includes audio system capabilities. In an embodiment, input 41162 related to at least one parameter of the vehicle includes screen size. In an embodiment, input 41162 related to at least one parameter of the vehicle includes route information. In an embodiment, input 41162 related to at least one parameter of the vehicle includes location information. In an embodiment, input 41163 related to at least one parameter of the rider includes rider demographic information.In an embodiment, the input 41163 related to at least one parameter of the rider includes the rider's emotional state. In an embodiment, the input 41163 related to at least one parameter of the rider includes the rider's reaction to pre-seat advertising. In an embodiment, the input 41163 related to at least one parameter of the rider includes the rider's social media activity.

[0373] Figure 43 shows a method 4300 for in-vehicle advertising according to embodiments of the systems and methods disclosed herein. In 4302, the method includes taking an input related to at least one parameter of a vehicle and an input related to at least one parameter of a lidar occupying the vehicle. In 4304, the method includes aggregating the inputs across multiple vehicles. In 4306, the method includes using a hybrid recognition system to determine in-vehicle advertising placement opportunities based on the aggregated inputs. In 4308, the method includes providing placement opportunities in an advertising network that facilitates bidding for placement opportunities. In 4309, the method includes delivering advertisements for placement within the vehicle interface based on the results of the bidding.

[0374] Referring to Figures 41 and 43, in an embodiment, the vehicle 4110 constitutes a system for automating at least one control parameter of the vehicle. In an embodiment, the vehicle 4110 is at least a semi-autonomous vehicle. In an embodiment, the vehicle 4110 is automatically routed. In an embodiment, the vehicle 4110 is an autonomous vehicle. In an embodiment, a first part 41165 of the hybrid perception system 41164 determines the operating state 41168 of the vehicle by processing an input 41162 related to at least one parameter of the vehicle. In an embodiment, a second part 41166 of the hybrid perception system 41164 determines the lidar state 41149 of the vehicle by processing an input 41163 related to at least one parameter of the lidar. In an embodiment, a third part 41167 of the hybrid recognition system 41164 determines, based on the vehicle (operational) state 41168 and the lidar state 41149, at least one of the price, classification, content and location of an advertisement to be delivered within the interface 41133 of the vehicle 4110 to the lidar 4144 of the seat 4128 of the vehicle 4110. In an embodiment, the advertisement is delivered by the winning advertiser. In an embodiment, the delivery of the advertisement is based on the winning bid. In an embodiment, an input 41162 related to at least one parameter of the vehicle includes a vehicle classification. In an embodiment, an input 41162 related to at least one parameter of the vehicle includes a display classification. In an embodiment, an input 41162 related to at least one parameter of the vehicle includes audio system capabilities. In an embodiment, an input 41162 related to at least one parameter of the vehicle includes screen size. In an embodiment, an input 41162 related to at least one parameter of the vehicle includes route information. In an embodiment, input 41162 related to at least one parameter of the vehicle includes location information. In an embodiment, input 41163 related to at least one parameter of the rider includes rider demographic information. In an embodiment, input 41163 related to at least one parameter of the rider includes rider emotional state.In one embodiment, the input 41163 relating to at least one parameter of the rider includes the rider's response to pre-seat advertising. In another embodiment, the input 41163 relating to at least one parameter of the rider includes the rider's social media activity.

[0375] Referring to Figure 44, an embodiment provided herein is a transport system 4411 having a motorcycle helmet 44170 configured to provide an augmented reality experience based on the registration of the position and orientation of a wearer 44172 in an environment 44171.

[0376] Embodiments provided herein include a transport system 4411 comprising a motorcycle helmet 44170 for providing an augmented reality experience based on the registration of the position and orientation of a helmet wearer 44172 in an environment 44171.

[0377] Embodiments provided herein include a motorcycle helmet 44170 comprising: a data processor 4488 configured to facilitate communication between a rider 44172 wearing the helmet 44170 and a motorcycle 44169, the data processor 4488 through which the motorcycle 44169 and the helmet 44170 communicate the position and orientation 44173 of the motorcycle 44169; and an augmented reality system 44174 comprising a display 44175 positioned to facilitate the presentation of augmented content in the environment 44171 of the rider wearing the helmet, the augmentation being responsive to the registration of the communicated position and orientation 44128 of the motorcycle 44169. In embodiments, at least one parameter of the augmentation is determined by machine learning relating to at least one input relating to at least one of the rider 44172 and the motorcycle 44180.

[0378] In an embodiment, the motorcycle 44169 includes a system for automating at least one control parameter of the motorcycle. In an embodiment, the motorcycle 44169 is at least a semi-autonomous motorcycle. In an embodiment, the motorcycle 44169 is automatically routed. In an embodiment, the motorcycle 44169 is an autonomous motorcycle. In an embodiment, the content in the environment is content that can be seen in a portion of the field of view of a helmeted rider. In an embodiment, machine learning on rider input determines the rider's emotional state, and the value for at least one parameter is adapted in response to the rider's emotional state. In an embodiment, machine learning on motorcycle input determines the operating state of the motorcycle, and the value for at least one parameter is adapted in response to the operating state of the motorcycle. In an embodiment, the helmet 44170 further comprises a motorcycle configuration expert system 44139 for recommending adjustments to the value of at least one parameter 44156 in response to at least one input to the augmented reality system.

[0379] Embodiments provided herein include a motorcycle helmet augmented reality system. The system includes a display 44175 positioned to facilitate the presentation of augmented content in the environment of a rider wearing a helmet; a circuit 4488 for registering at least one of the position and orientation of the motorcycle the rider is riding; a machine learning circuit 44179 for determining at least one augmentation parameter 44156 by processing at least one input related to at least one of the rider 44163 and the motorcycle 44180; a reality augmentation circuit 4488 for generating an augmentation element 44155 in response to at least one of the position and orientation of the rider 44163; and a reality augmentation circuit 4488 for generating an augmentation element 44177 for presentation on the display 44175 in response to at least one of the registered position and orientation of the motorcycle, wherein the generation is at least partially based on the determined at least one augmentation parameter 44156.

[0380] In an embodiment, the motorcycle 44169 includes a system for automating at least one control parameter of the motorcycle. In an embodiment, the motorcycle 44169 is at least a semi-autonomous motorcycle. In an embodiment, the motorcycle 44169 is automatically routed. In an embodiment, the motorcycle 44169 is an autonomous motorcycle. In an embodiment, the content 44176 in the environment is content that can be seen in part of the field of view of a helmeted rider 44172. In an embodiment, machine learning on rider input determines the rider's emotional state, and a value for at least one parameter is adapted in response to the rider's emotional state. In an embodiment, machine learning on motorcycle input determines the motorcycle's operating state, and a value for at least one parameter is adapted in response to the motorcycle's operating state.

[0381] In one embodiment, the helmet further comprises a motorcycle configuration expert system 44139 for recommending an augmented reality system 4488 to adjust the value of at least one parameter 44156 in response to at least one input.

[0382] In an embodiment, leveraging network technology for a transport system may support cognitive collective charging or refueling planning for vehicles within the transport system. Such a transport system may include an artificial intelligence system for taking inputs related to multiple vehicles, such as autonomous vehicles, and determining at least one parameter of a charging or refueling plan for at least one of the multiple vehicles based on the inputs.

[0383] In embodiments, the transport system may be a vehicle transport system. Such a vehicle transport system may include a network-enabled vehicle information ingestion port 4532 that can provide a network (e.g., the Internet) interface from which inputs such as operating status and energy consumption information from at least one of a plurality of network-enabled vehicles 4510 can be collected. In embodiments, such inputs may be collected in real time as the plurality of network-enabled vehicles 4510 connect and distribute vehicle operating status, energy consumption, and other relevant information. In embodiments, the inputs may relate to vehicle energy consumption and may be determined from the battery charge status of some of the plurality of vehicles. The inputs may include vehicle route plans, indicators of vehicle charge values, etc. The inputs may include predicted traffic conditions for the plurality of vehicles. The transport system may also include a vehicle charging or refueling infrastructure that may include one or more vehicle charging infrastructure control systems 4534. These control systems 4534 may receive operating status and energy consumption information from the plurality of network-enabled vehicles 4510 via the ingestion port 4532 or directly via a common or set of connected networks such as the Internet. Such a transport system may further include an artificial intelligence system 4536 that is functionally connected to a vehicle charging infrastructure control system 4534, which may determine, provide, adjust, or create at least one charging plan parameter 4514 on which a charging plan 4512 for at least some of a plurality of network-enabled vehicles 4510 depends, for example, in response to the reception of operating status and energy consumption information. This dependency may result in changes in the application of the charging plan 4512 by the control system 4534, such as when the processor of the control system 4534 executes a program derived from or based on the charging plan 4512.The charging infrastructure control system(s) 4534 may include a cloud-based computing system that is remote from the charging infrastructure system (e.g., remote from an electric vehicle charging kiosk); it may also include a local charging infrastructure system 4538 that is located with and / or integrated with infrastructure elements such as fuel stations and charging kiosks. In embodiments, the artificial intelligence system 4536 may interface with and coordinate the cloud-based system 4534, the local charging infrastructure system 4538, or both. In embodiments, the coordination of the cloud-based system may take the form of a different interface than coordination with the local charging infrastructure system 4538, such as providing parameters that affect multiple charging kiosks, and may provide information that the local system can use to adapt charging system control commands, for example, that may be provided from the cloud-based control system 4534. For example, the cloud-based control system (which may control only a portion of the available charging / refueling ...

Claims

1. A system for presenting a set of vehicle operating states to a user of a vehicle that has operating states, To determine the vehicle's operating state, a digital twin receives vehicle parameter data from one or more inputs, An interface for the digital twin for presenting the vehicle's operating status to the user of the vehicle, Includes an identity management system that manages a set of identities and roles for the user of the vehicle, evaluates user attributes in accordance with access policies based on an analysis of the set of identities and roles, and regulates the user's access level for the vehicle based on the evaluation, thereby determining the ability to view, modify, and configure the digital twin so that useful information is provided to the user of the vehicle. Furthermore, a first neural network detects the satisfaction level of the rider user riding in the vehicle through analysis of data collected from sensors installed in the vehicle to collect the physiological state of the rider user, To achieve the rider user's preferred state of satisfaction, the system includes a second neural network that optimizes the vehicle's operating parameters that affect the vehicle's operating state based on the correlation between the vehicle's operating state and the rider user's satisfaction state, according to the detected state of satisfaction of the rider user. The detected satisfaction state of the Rider user is the detected emotional state of the Rider user, and the preferred satisfaction state of the Rider user is the preferred emotional state of the Rider user. A system characterized in that the physiological state is the stress level or cortisol level of the rider user.

2. The system according to claim 1, characterized in that the vehicle operating state is a vehicle maintenance state.

3. The system according to claim 1, characterized in that the vehicle operating state is a vehicle energy utilization state.

4. The system according to claim 1, characterized in that the vehicle operating state is the vehicle navigation state.

5. The system according to claim 1, characterized in that the vehicle operating state is the vehicle component state.

6. The system according to claim 1, characterized in that the vehicle operating state is the vehicle driver state.

7. The system according to claim 1, characterized in that the input for the digital twin includes at least one of the following: an in-vehicle diagnostic system, a telemetry system, a vehicle-mounted sensor, or a system outside the vehicle.

8. The system according to claim 1, characterized in that the digital twin is implemented via an API from the vehicle's edge intelligence system, which provides connectivity to systems outside the vehicle.

9. The system according to claim 1, characterized in that the digital twin is implemented via API from an edge intelligence system of the vehicle that provides internal connectivity to a set of sensors and data sources of the vehicle.

10. The system according to claim 1, characterized in that the digital twin is implemented via an API from an edge intelligence system of the vehicle that provides connectivity to an in-vehicle artificial intelligence system.

11. The system according to claim 10, characterized in that the digital twin is automatically configured by an artificial intelligence system to provide useful information to the user of the vehicle, based on a training set of usage activities by a set of digital twin users, using a model that has learned the training set as input.

12. The system according to claim 10, characterized in that the digital twin is automatically configured by an artificial intelligence system to provide useful information to the user of the vehicle, based on a training set of usage activities by the driver user, using a model that has learned using the training set as input.

13. The system according to claim 10, characterized in that the digital twin is automatically configured by an artificial intelligence system to provide useful information to the user of the vehicle, based on a training set of usage activities by the rider user, using a model that has learned using the training set as input.

14. The system according to claim 1, characterized in that the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network.

15. The system according to claim 1, characterized in that at least one of the neural networks is a hybrid neural network, and includes a convolutional neural network.

16. The system according to claim 1, characterized in that the second neural network optimizes the operating parameters in real time in response to the detection of the Rider user's satisfaction state by the first neural network.

17. The system according to claim 1, wherein the first neural network includes a plurality of connected nodes that form a directed cycle, and the first neural network further facilitates the bidirectional flow of data between the connected nodes.

18. The system according to claim 1, characterized in that the optimized operating parameter affects at least one of the following: the vehicle's path, the in-vehicle audio content, the vehicle's speed, the vehicle's acceleration, the vehicle's deceleration, its approach to an object along the path, or its approach to another vehicle along the path.

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